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Academic literature on the topic 'Image classification tasks'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Image classification tasks"

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Wang, Liangliang, and Deepu Rajan. "An image similarity descriptor for classification tasks." Journal of Visual Communication and Image Representation 71 (August 2020): 102847. http://dx.doi.org/10.1016/j.jvcir.2020.102847.

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Li, Chuanlong, Xiufen Ye, Jier Xi, and Yunpeng Jia. "A Texture Feature Removal Network for Sonar Image Classification and Detection." Remote Sensing 15, no. 3 (January 20, 2023): 616. http://dx.doi.org/10.3390/rs15030616.

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Deep neural network (DNN) was applied in sonar image target recognition tasks, but it is very difficult to obtain enough sonar images that contain a target; as a result, the direct use of a small amount of data to train a DNN will cause overfitting and other problems. Transfer learning is the most effective way to address such scenarios. However, there is a large domain gap between optical images and sonar images, and common transfer learning methods may not be able to effectively handle it. In this paper, we propose a transfer learning method for sonar image classification and object detection called the texture feature removal network. We regard the texture features of an image as domain-specific features, and we narrow the domain gap by discarding the domain-specific features, and hence, make it easier to complete knowledge transfer. Our method can be easily embedded into other transfer learning methods, which makes it easier to apply to different application scenarios. Experimental results show that our method is effective in side-scan sonar image classification tasks and forward-looking sonar image detection tasks. For side-scan sonar image classification tasks, the classification accuracy of our method is enhanced by 4.5% in a supervised learning experiment, and for forward-looking sonar detection tasks, the average precision (AP) is also significantly improved.
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Zhang, Taohong, Suli Fan, Junnan Hu, Xuxu Guo, Qianqian Li, Ying Zhang, and Aziguli Wulamu. "A Feature Fusion Method with Guided Training for Classification Tasks." Computational Intelligence and Neuroscience 2021 (April 14, 2021): 1–11. http://dx.doi.org/10.1155/2021/6647220.

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In this paper, a feature fusion method with guiding training (FGT-Net) is constructed to fuse image data and numerical data for some specific recognition tasks which cannot be classified accurately only according to images. The proposed structure is divided into the shared weight network part, the feature fused layer part, and the classification layer part. First, the guided training method is proposed to optimize the training process, the representative images and training images are input into the shared weight network to learn the ability that extracts the image features better, and then the image features and numerical features are fused together in the feature fused layer to input into the classification layer for the classification task. Experiments are carried out to verify the effectiveness of the proposed model. Loss is calculated by the output of both the shared weight network and classification layer. The results of experiments show that the proposed FGT-Net achieves the accuracy of 87.8%, which is 15% higher than the CNN model of ShuffleNetv2 (which can process image data only) and 9.8% higher than the DNN method (which processes structured data only).
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Tang, Chaohui, Qingxin Zhu, Wenjun Wu, Wenlin Huang, Chaoqun Hong, and Xinzheng Niu. "PLANET: Improved Convolutional Neural Networks with Image Enhancement for Image Classification." Mathematical Problems in Engineering 2020 (March 11, 2020): 1–10. http://dx.doi.org/10.1155/2020/1245924.

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In the past few years, deep learning has become a research hotspot and has had a profound impact on computer vision. Deep CNN has been proven to be the most important and effective model for image processing, but due to the lack of training samples and huge number of learning parameters, it is easy to tend to overfit. In this work, we propose a new two-stage CNN image classification network, named “Improved Convolutional Neural Networks with Image Enhancement for Image Classification” and PLANET in abbreviation, which uses a new image data enhancement method called InnerMove to enhance images and augment the number of training samples. InnerMove is inspired by the “object movement” scene in computer vision and can improve the generalization ability of deep CNN models for image classification tasks. Sufficient experiment results show that PLANET utilizing InnerMove for image enhancement outperforms the comparative algorithms, and InnerMove has a more significant effect than the comparative data enhancement methods for image classification tasks.
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Zhou, Lanfeng, Ziwei Liu, and Wenfeng Wang. "Terrain Classification Algorithm for Lunar Rover Using a Deep Ensemble Network with High-Resolution Features and Interdependencies between Channels." Wireless Communications and Mobile Computing 2020 (October 13, 2020): 1–14. http://dx.doi.org/10.1155/2020/8842227.

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For terrain classification tasks, previous methods used a single scale or single model to extract the features of the image, used high-to-low resolution networks to extract the features of the image, and used a network with no relationship between channels. These methods would lead to the inadequacy of the extracted features. Therefore, classification accuracy would reduce. The samples in terrain classification tasks are different from in other image classification tasks. The differences between samples in terrain classification tasks are subtler than other image-level classification tasks. And the colours of each sample in the terrain classification are similar. So we need to maintain the high resolution of features and establish the interdependencies between the channels to highlight the image features. This kind of networks can improve classification accuracy. To overcome these challenges, this paper presents a terrain classification algorithm for Lunar Rover by using a deep ensemble network. We optimize the activation function and the structure of the convolutional neural network to make it better to extract fine features of the images and infer the terrain category of the image. In particular, several contributions are made in this paper: establishing interdependencies between channels to highlight features and maintaining a high-resolution representation throughout the process to ensure the extraction of fine features. Multimodel collaborative judgment can help make up for the shortcomings in the design of the single model structure, make the model form a competitive relationship, and improve the accuracy. The overall classification accuracy of this method reaches 91.57% on our dataset, and the accuracy is higher on some terrains.
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Melekhin, V. B., and V. M. Khachumov. "Stable descriptors in image recognition tasks." Herald of Dagestan State Technical University. Technical Sciences 47, no. 3 (October 1, 2020): 93–100. http://dx.doi.org/10.21822/2073-6185-2020-47-3-93-100.

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Objective. The objective of the study is to determine various stable characteristics of images (semi-invariants and invariants) as descriptors necessary for the formation of a feature space of standards intended for recognizing images of different nature belonging to different classes of objects. Methods. The authors propose metrics for evaluating the proximity of the recognized image to a given standard in the space of covariance matrices, based on the obtained descriptors as a methodological basis for constructing image recognition methods. Results. The content of the main stages of selecting descriptors for a given class of objects is developed, taking into account the different illumination of the recognized images. The effectiveness of the results obtained is confirmed by experimental studies related to the solution of the problem of recognition of special images - facies. Conclusions. The definition of stable image descriptors as invariants or semi-invariants to zoom and brightness transformations allows solving the problems of facies classification in conditions of the unstable shooting of recognized images. The images can be rotated and shifted in any way. In general, the proposed approach allows developing an effective image recognition system in the presence of various types of interference on the recognized images.
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Singh, Ankita, and Pawan Singh. "Image Classification: A Survey." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 1, no. 2 (November 19, 2020): 1–9. http://dx.doi.org/10.54060/jieee/001.02.002.

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The Classification of images is a paramount topic in artificial vision systems which have drawn a notable amount of interest over the past years. This field aims to classify an image, which is an input, based on its visual content. Currently, most people relied on hand-crafted features to describe an image in a particular way. Then, using classifiers that are learnable, such as random forest, and decision tree was applied to the extract features to come to a final decision. The problem arises when large numbers of photos are concerned. It becomes a too difficult problem to find features from them. This is one of the reasons that the deep neural network model has been introduced. Owing to the existence of Deep learning, it can become feasible to represent the hierarchical nature of features using a various number of layers and corresponding weight with them. The existing image classification methods have been gradually applied in real-world problems, but then there are various problems in its application processes, such as unsatisfactory effect and extremely low classification accuracy or then and weak adaptive ability. Models using deep learning concepts have robust learning ability, which combines the feature extraction and the process of classification into a whole which then completes an image classification task, which can improve the image classification accuracy effectively. Convolutional Neural Networks are a powerful deep neural network technique. These networks preserve the spatial structure of a problem and were built for object recognition tasks such as classifying an image into respective classes. Neural networks are much known because people are getting a state-of-the-art outcome on complex computer vision and natural language processing tasks. Convolutional neural networks have been extensively used.
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Yan, Yang, Wen Bo Huang, Yun Ji Wang, and Na Li. "Image Labeling Model Based on Conditional Random Fields." Advanced Materials Research 756-759 (September 2013): 3869–73. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3869.

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We present conditional random fields (CRFs), a framework for building probabilistic models to segment and label sequence data, and use CRFs to label pixels in an image. CRFs provide a discriminative framework to incorporate spatial dependencies in an image, which is more appropriate for classification tasks as opposed to a generative framework. In this paper we apply CRF to an image classification tasks: an image labeling problem (manmade vs. natural regions in the MSRC 21-object class datasets). Parameter learning is performed using contrastive divergence (CD) algorithm to maximize an approximation to the conditional likelihood. We focus on two aspects of the classification task: feature extraction and classifiers design. We present classification results on sample images from MSRC 21-object class datasets.
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Elizarov, Artem Aleksandrovich, and Evgenii Viktorovich Razinkov. "Image Classification Using Reinforcement Learning." Russian Digital Libraries Journal 23, no. 6 (May 12, 2020): 1172–91. http://dx.doi.org/10.26907/1562-5419-2020-23-6-1172-1191.

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Recently, such a direction of machine learning as reinforcement learning has been actively developing. As a consequence, attempts are being made to use reinforcement learning for solving computer vision problems, in particular for solving the problem of image classification. The tasks of computer vision are currently one of the most urgent tasks of artificial intelligence. The article proposes a method for image classification in the form of a deep neural network using reinforcement learning. The idea of ​​the developed method comes down to solving the problem of a contextual multi-armed bandit using various strategies for achieving a compromise between exploitation and research and reinforcement learning algorithms. Strategies such as -greedy, -softmax, -decay-softmax, and the UCB1 method, and reinforcement learning algorithms such as DQN, REINFORCE, and A2C are considered. The analysis of the influence of various parameters on the efficiency of the method is carried out, and options for further development of the method are proposed.
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Yan, Yang, Wen Bo Huang, and Yun Ji Wang. "Image Classification Based on Conditional Random Fields." Applied Mechanics and Materials 556-562 (May 2014): 4901–5. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4901.

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We use Conditional Random Fields (CRFs) to classify regions in an image. CRFs provide a discriminative framework to incorporate spatial dependencies in an image, which is more appropriate for classification tasks as opposed to a generative framework. In this paper we apply CRFs to the image multi-classification task, we focus on three aspects of the classification task: feature extraction, the Original feature clustering based on K-means, and feature vector modeling base on CRF to obtain multiclass classification. We present classification results on sample images from Cambridge (MSRC) database, and the experimental results show that the method we present can classify the images accurately.
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Dissertations / Theses on the topic "Image classification tasks"

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Ye, Meng. "VISUAL AND SEMANTIC KNOWLEDGE TRANSFER FOR NOVEL TASKS." Diss., Temple University Libraries, 2019. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/583037.

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Computer and Information Science
Ph.D.
Data is a critical component in a supervised machine learning system. Many successful applications of learning systems on various tasks are based on a large amount of labeled data. For example, deep convolutional neural networks have surpassed human performance on ImageNet classification, which consists of millions of labeled images. However, one challenge in conventional supervised learning systems is their generalization ability. Once a model is trained on a specific dataset, it can only perform the task on those \emph{seen} classes and cannot be used for novel \emph{unseen} classes. In order to make the model work on new classes, one has to collect and label new data and then re-train the model. However, collecting data and labeling them is labor-intensive and costly, in some cases, it is even impossible. Also, there is an enormous amount of different tasks in the real world. It is not applicable to create a dataset for each of them. These problems raise the need for Transfer Learning, which is aimed at using data from the \emph{source} domain to improve the performance of a model on the \emph{target} domain, and these two domains have different data or different tasks. One specific case of transfer learning is Zero-Shot Learning. It deals with the situation where \emph{source} domain and \emph{target} domain have the same data distribution but do not have the same set of classes. For example, a model is given animal images of `cat' and `dog' for training and will be tested on classifying 'tiger' and 'wolf' images, which it has never seen. Different from conventional supervised learning, Zero-Shot Learning does not require training data in the \emph{target} domain to perform classification. This property gives ZSL the potential to be broadly applied in various applications where a system is expected to tackle unexpected situations. In this dissertation, we develop algorithms that can help a model effectively transfer visual and semantic knowledge learned from \emph{source} task to \emph{target} task. More specifically, first we develop a model that learns a uniform visual representation of semantic attributes, which help alleviate the domain shift problem in Zero-Shot Learning. Second, we develop an ensemble network architecture with a progressive training scheme, which transfers \emph{source} domain knowledge to the \emph{target} domain in an end-to-end manner. Lastly, we move a step beyond ZSL and explore Label-less Classification, which transfers knowledge from pre-trained object detectors into scene classification tasks. Our label-less classification takes advantage of word embeddings trained from unorganized online text, thus eliminating the need for expert-defined semantic attributes for each class. Through comprehensive experiments, we show that the proposed methods can effectively transfer visual and semantic knowledge between tasks, and achieve state-of-the-art performances on standard datasets.
Temple University--Theses
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Opatřilová, Irena. "Metodika řešení masivních úloh v GIS." Doctoral thesis, Vysoké učení technické v Brně. Fakulta stavební, 2015. http://www.nusl.cz/ntk/nusl-234546.

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This doctoral thesis deals with the issue of solving massive tasks in GIS. These tasks process large volumes of geographic data with different formats. The thesis describes a theoretical analysis of the complexity of tasks and the possibilities to optimize sub-processes which lead to an acceptable solution. It considers the possibility of using parallelism in GIS, which leads to an acceleration in the processing of large volumes of geographic data. It also proposes a method for the optimization of processes through an algorithm which determines the number of means necessary for the successful solution of a task at a specified time and assigns processes to these means. Additionally, there is a proposed algorithm for the optimization of the preparation of data for extensive GIS projects. The algorithms have been validated by the results of a research project, the aim of which was to analyse the terrain surface above a gas line in the Czech Republic. The primary method of analysis was the classification of an orthophoto image, which was further refined through filtration using the ZABAGED layers. Therefore, the thesis deals with the possibility of improving the results of image classification using GIS instruments as well as dealing with the determination of the error rate in analysis results. The results of the analysis are now used for the strategic planning of maintenance and the development of gas facilities in the Czech Republic. The results of the work have general importance regarding the performance of other operations of the same class in GIS.
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Schoening, Timm [Verfasser]. "Automated detection in benthic images for megafauna classification and marine resource exploration: supervised and unsupervised methods for classification and regression tasks in benthic images with efficient integration of expert knowledge / Timm Schoening." Bielefeld : Universitätsbibliothek Bielefeld, 2015. http://d-nb.info/1068001402/34.

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Mulgrew, Kate Elizabeth. "Attention and memory bias for body image and health related information using an Emotional Stroop task in a non-clinical sample." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/26964/1/Kate_Mulgrew_Thesis.pdf.

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It has been proposed that body image disturbance is a form of cognitive bias wherein schemas for self-relevant information guide the selective processing of appearancerelated information in the environment. This threatening information receives disproportionately more attention and memory, as measured by an Emotional Stroop and incidental recall task. The aim of this thesis was to expand the literature on cognitive processing biases in non-clinical males and females by incorporating a number of significant methodological refinements. To achieve this aim, three phases of research were conducted. The initial two phases of research provided preliminary data to inform the development of the main study. Phase One was a qualitative exploration of body image concerns amongst males and females recruited through the general community and from a university. Seventeen participants (eight male; nine female) provided information on their body image and what factors they saw as positively and negatively impacting on their self evaluations. The importance of self esteem, mood, health and fitness, and recognition of the social ideal were identified as key themes. These themes were incorporated as psycho-social measures and Stroop word stimuli in subsequent phases of the research. Phase Two involved the selection and testing of stimuli to be used in the Emotional Stroop task. Six experimental categories of words were developed that reflected a broad range of health and body image concerns for males and females. These categories were high and low calorie food words, positive and negative appearance words, negative emotion words, and physical activity words. Phase Three addressed the central aim of the project by examining cognitive biases for body image information in empirically defined sub-groups. A National sample of males (N = 55) and females (N = 144), recruited from the general community and universities, completed an Emotional Stroop task, incidental memory test, and a collection of psycho-social questionnaires. Sub-groups of body image disturbance were sought using a cluster analysis, which identified three sub-groups in males (Normal, Dissatisfied, and Athletic) and four sub-groups in females (Normal, Health Conscious, Dissatisfied, and Symptomatic). No differences were noted between the groups in selective attention, although time taken to colour name the words was associated with some of the psycho-social variables. Memory biases found across the whole sample for negative emotion, low calorie food, and negative appearance words were interpreted as reflecting the current focus on health and stigma against being unattractive. Collectively these results have expanded our understanding of processing biases in the general community by demonstrating that the processing biases are found within non-clinical samples and that not all processing biases are associated with negative functionality
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Mulgrew, Kate Elizabeth. "Attention and memory bias for body image and health related information using an Emotional Stroop task in a non-clinical sample." Queensland University of Technology, 2008. http://eprints.qut.edu.au/26964/.

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It has been proposed that body image disturbance is a form of cognitive bias wherein schemas for self-relevant information guide the selective processing of appearancerelated information in the environment. This threatening information receives disproportionately more attention and memory, as measured by an Emotional Stroop and incidental recall task. The aim of this thesis was to expand the literature on cognitive processing biases in non-clinical males and females by incorporating a number of significant methodological refinements. To achieve this aim, three phases of research were conducted. The initial two phases of research provided preliminary data to inform the development of the main study. Phase One was a qualitative exploration of body image concerns amongst males and females recruited through the general community and from a university. Seventeen participants (eight male; nine female) provided information on their body image and what factors they saw as positively and negatively impacting on their self evaluations. The importance of self esteem, mood, health and fitness, and recognition of the social ideal were identified as key themes. These themes were incorporated as psycho-social measures and Stroop word stimuli in subsequent phases of the research. Phase Two involved the selection and testing of stimuli to be used in the Emotional Stroop task. Six experimental categories of words were developed that reflected a broad range of health and body image concerns for males and females. These categories were high and low calorie food words, positive and negative appearance words, negative emotion words, and physical activity words. Phase Three addressed the central aim of the project by examining cognitive biases for body image information in empirically defined sub-groups. A National sample of males (N = 55) and females (N = 144), recruited from the general community and universities, completed an Emotional Stroop task, incidental memory test, and a collection of psycho-social questionnaires. Sub-groups of body image disturbance were sought using a cluster analysis, which identified three sub-groups in males (Normal, Dissatisfied, and Athletic) and four sub-groups in females (Normal, Health Conscious, Dissatisfied, and Symptomatic). No differences were noted between the groups in selective attention, although time taken to colour name the words was associated with some of the psycho-social variables. Memory biases found across the whole sample for negative emotion, low calorie food, and negative appearance words were interpreted as reflecting the current focus on health and stigma against being unattractive. Collectively these results have expanded our understanding of processing biases in the general community by demonstrating that the processing biases are found within non-clinical samples and that not all processing biases are associated with negative functionality
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Mattia, Carmine. "Exploring CNNs: an application study on nuclei recognition task in colon cancer histology images." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12262/.

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In this work we explore the recent advances in the field of Convolutional Neural Network (CNN), with particular interest to the task of image classification. Moreover, we explore a new neural network algorithm, called ladder network, which enables the semi-supervised framework on pre-existing neural networks. These techniques were applied to a task of nuclei classification in routine colon cancer histology images. Specifically, starting from an existing CNN developed for this purpose, we improve its performances utilizing a better data augmentation, a more efficient initialization of the network and adding the batch normalization layer. These improvements were made to achieve a state-of-the-art architecture which could be compatible with the ladder network algorithm. A specific custom version of the ladder network algorithm was implemented in our CNN in order to use the amount of data without a label presented with the used database. However we observed a deterioration of the performances using the unlabeled examples of this database, probably due to a distribution bias in them compared to the labeled ones. Even without using of the semi-supervised framework, the ladder algorithm allows to obtain a better representation in the CNN which leads to a dramatic performance improvement of the starting CNN algorithm. We reach this result only with a little increase in complexity of the final model, working specifically on the training process of the algorithm.
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Book chapters on the topic "Image classification tasks"

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Xu, Long, Yihua Yan, and Xin Huang. "Deep Learning in Solar Image Classification Tasks." In Deep Learning in Solar Astronomy, 19–40. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2746-1_3.

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Madrid, Jorge G., and Hugo Jair Escalante. "Meta-learning of Text Classification Tasks." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 107–19. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33904-3_10.

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Piras, Luca, and Giorgio Giacinto. "Open Issues on Codebook Generation in Image Classification Tasks." In Machine Learning and Data Mining in Pattern Recognition, 328–42. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08979-9_25.

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Bahroun, Yanis, Eugénie Hunsicker, and Andrea Soltoggio. "Building Efficient Deep Hebbian Networks for Image Classification Tasks." In Artificial Neural Networks and Machine Learning – ICANN 2017, 364–72. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68600-4_42.

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Smith, Kaleb E., Phillip Williams, Tatsanee Chaiya, and Max Ble. "Deep Convolutional-Shepard Interpolation Neural Networks for Image Classification Tasks." In Lecture Notes in Computer Science, 185–92. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93000-8_21.

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Zenk, Maximilian, David Zimmerer, Fabian Isensee, Paul F. Jäger, Jakob Wasserthal, and Klaus Maier-Hein. "Realistic Evaluation of FixMatch on Imbalanced Medical Image Classification Tasks." In Informatik aktuell, 291–96. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-36932-3_61.

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Lange, Sascha, and Martin Riedmiller. "Evolution of Computer Vision Subsystems in Robot Navigation and Image Classification Tasks." In RoboCup 2004: Robot Soccer World Cup VIII, 184–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32256-6_15.

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Candemir, Cemre, Osman Tayfun Bişkin, Mustafa Alper Selver, and All Saffet Gönül. "Automatic Classification of fMRI Signals from Behavioral, Cognitive and Affective Tasks Using Deep Learning." In Convolutional Neural Networks for Medical Image Processing Applications, 133–54. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003215141-7.

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Hashemi, Atiye Sadat, Andreas Bär, Saeed Mozaffari, and Tim Fingscheidt. "Improving Transferability of Generated Universal Adversarial Perturbations for Image Classification and Segmentation." In Deep Neural Networks and Data for Automated Driving, 171–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_6.

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AbstractAlthough deep neural networks (DNNs) are high-performance methods for various complex tasks, e.g., environment perception in automated vehicles (AVs), they are vulnerable to adversarial perturbations. Recent works have proven the existence of universal adversarial perturbations (UAPs), which, when added to most images, destroy the output of the respective perception function. Existing attack methods often show a low success rate when attacking target models which are different from the one that the attack was optimized on. To address such weak transferability, we propose a novel learning criterion by combining a low-level feature loss, addressing the similarity of feature representations in the first layer of various model architectures, with a cross-entropy loss. Experimental results on ImageNet and Cityscapes datasets show that our method effectively generates universal adversarial perturbations achieving state-of-the-art fooling rates across different models, tasks, and datasets. Due to their effectiveness, we propose the use of such novel generated UAPs in robustness evaluation of DNN-based environment perception functions for AVs.
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Soni, Rituraj, and Deepak Sharma. "Building Machine Learning Models for Classification of Text and Non-text Elements in Natural Scene Images." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 955–68. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_97.

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AbstractComputer vision aims to build autonomous systems that can perform some of the human visual system’s tasks (and even surpass it in many cases)among the several applications of Computer Vision, extracting the information from the natural scene images is famous and influential. The information gained from an image can vary from identification, space measurements for navigation, or augmented reality applications. These scene images contain relevant text elements as well as many non-text elements. Prior to extracting meaningful information from the text, the foremost task is to classify the text & non-text elements correctly in the given images. The present paper aims to build machine learning models for accurately organizing the text and non-text elements in the benchmark dataset ICDAR 2013. The result is obtained in terms of the confusion matrix to determine the overall accuracy of the different machine learning models.
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Conference papers on the topic "Image classification tasks"

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Feng, Zunlei, Tian Qiu, Sai Wu, Xiaotuan Jin, Zengliang He, Mingli Song, and Huiqiong Wang. "Comparison Knowledge Translation for Generalizable Image Classification." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/411.

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Deep learning has recently achieved remarkable performance in image classification tasks, which depends heavily on massive annotation. However, the classification mechanism of existing deep learning models seems to contrast to humans' recognition mechanism. With only a glance at an image of the object even unknown type, humans can quickly and precisely find other same category objects from massive images, which benefits from daily recognition of various objects. In this paper, we attempt to build a generalizable framework that emulates the humans' recognition mechanism in the image classification task, hoping to improve the classification performance on unseen categories with the support of annotations of other categories. Specifically, we investigate a new task termed Comparison Knowledge Translation (CKT). Given a set of fully labeled categories, CKT aims to translate the comparison knowledge learned from the labeled categories to a set of novel categories. To this end, we put forward a Comparison Classification Translation Network (CCT-Net), which comprises a comparison classifier and a matching discriminator. The comparison classifier is devised to classify whether two images belong to the same category or not, while the matching discriminator works together in an adversarial manner to ensure whether classified results match the truth. Exhaustive experiments show that CCT-Net achieves surprising generalization ability on unseen categories and SOTA performance on target categories.
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Jain, Arjit, Pranay Reddy Samala, Preethi Jyothi, Deepak Mittal, and Maneesh Singh. "Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image Captioning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/105.

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Recent semi-supervised learning (SSL) methods are predominantly focused on multi-class classification tasks. Classification tasks allow for easy mixing of class labels during augmentation which does not trivially extend to structured outputs such as word sequences that appear in tasks like image captioning. Noisy Student Training is a recent SSL paradigm proposed for image classification that is an extension of self-training and teacher-student learning. In this work, we provide an in-depth analysis of the noisy student SSL framework for the task of image captioning and derive state-of-the-art results. The original algorithm relies on computationally expensive data augmentation steps that involve perturbing the raw images and computing features for each perturbed image. We show that, even in the absence of raw image augmentation, the use of simple model and feature perturbations to the input images for the student model are beneficial to SSL training. We also show how a paraphrase generator could be effectively used for label augmentation to improve the quality of pseudo labels and significantly improve performance. Our final results in the limited labeled data setting (1% of the MS-COCO labeled data) outperform previous state-of-the-art approaches by 2.5 on BLEU4 and 11.5 on CIDEr scores.
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Ubeda, A., E. Ianez, and J. M. Azorin. "Mental tasks classification for BCI using image correlation." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6091555.

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Huang, Bailiang, Yan Piao, Hao Zhang, and Baolin Tan. "Gaussian stochastic pooling method for image classification tasks." In 6th International Conference on Mechatronics and Intelligent Robotics, edited by Srikanta Patnaik and Tao Shen. SPIE, 2022. http://dx.doi.org/10.1117/12.2644592.

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Wang, S., L. Sun, W. Fan, J. Sun, S. Naoi, K. Shirahata, T. Fukagai, Y. Tomita, A. Ike, and T. Hashimoto. "An automated CNN recommendation system for image classification tasks." In 2017 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2017. http://dx.doi.org/10.1109/icme.2017.8019347.

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He, Ming, Guangyi Lv, Weidong He, Jianping Fan, and Guihua Zeng. "DeepME: Deep Mixture Experts for Large-scale Image Classification." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/100.

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Although deep learning has demonstrated its outstanding performance on image classification, most well-known deep networks make efforts to optimize both their structures and their node weights for recognizing fewer (e.g., no more than 1000) object classes. Therefore, it is attractive to extend or mixture such well-known deep networks to support large-scale image classification. According to our best knowledge, how to adaptively and effectively fuse multiple CNNs for large-scale image classification is still under-explored. On this basis, a deep mixture algorithm is developed to support large-scale image classification in this paper. First, a soft spectral clustering method is developed to construct a two-layer ontology (group layer and category layer) by assigning large numbers of image categories into a set of groups according to their inter-category semantic correlations, where the semantically-related image categories under the neighbouring group nodes may share similar learning complexities. Then, such two-layer ontology is further used to generate the task groups, in which each task group contains partial image categories with similar learning complexities and one particular base deep network is learned. Finally, a gate network is learned to combine all base deep networks with fewer diverse outputs to generate a mixture network with larger outputs. Our experimental results on ImageNet10K have demonstrated that our proposed deep mixture algorithm can achieve very competitive results (top 1 accuracy: 32.13%) on large-scale image classification tasks.
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Chen, Pei-Hung, and Shen-Shyang Ho. "Is overfeat useful for image-based surface defect classification tasks?" In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7532457.

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Nery, M. S., A. M. Machado, M. F. M. Campos, F. L. C. Padua, R. Carceroni, and J. P. Queiroz-Neto. "Determining the Appropriate Feature Set for Fish Classification Tasks." In XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05). IEEE, 2005. http://dx.doi.org/10.1109/sibgrapi.2005.25.

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Izquierdo-Cordova, Ramon, and Walterio Mayol-Cuevas. "Filter Distribution Templates in Convolutional Networks for Image Classification Tasks." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2021. http://dx.doi.org/10.1109/cvprw53098.2021.00136.

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Lee, Changwoo, and Ki-Seok Chung. "Score-based Aggregation for Attention Modules in Image Classification Tasks." In 2019 IEEE 4th International Conference on Technology, Informatics, Management, Engineering & Environment (TIME-E). IEEE, 2019. http://dx.doi.org/10.1109/time-e47986.2019.9353302.

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Reports on the topic "Image classification tasks"

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Бережна, Маргарита Василівна. The Destroyer Psycholinguistic Archetype. Baltija Publishing, 2021. http://dx.doi.org/10.31812/123456789/6036.

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The aim of the research is to identify the elements of the psycholinguistic image of the main antagonist Hela in the superhero film Thor: Ragnarok based on the Marvel Comics and directed by Taika Waititi (2017). The task consists of two stages, at the first of which I identify the psychological characteristics of the character to determine to which of the archetypes Hela belongs. As the basis, I take the classification of film archetypes by V. Schmidt. At the second stage, I distinguish the speech peculiarities of the character that reflect her psychological image.
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Бережна, Маргарита Василівна. Maleficent: from the Matriarch to the Scorned Woman (Psycholinguistic Image). Baltija Publishing, 2021. http://dx.doi.org/10.31812/123456789/5766.

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The aim of the research is to identify the elements of the psycholinguistic image of the leading character in the dark fantasy adventure film Maleficent directed by Robert Stromberg (2014). The task consists of two stages, at the first of which I identify the psychological characteristics of the character to determine to which of the archetypes Maleficent belongs. As the basis, I take the classification of film archetypes by V. Schmidt. At the second stage, I distinguish the speech peculiarities of the character that reflex her psychological image. This paper explores 98 Maleficent’s turns of dialogues in the film. According to V. Schmidt’s classification, Maleficent belongs first to the Matriarch archetype and later in the plot to the Scorned Woman archetype. These archetypes are representations of the powerful goddess of marriage and fertility Hera, being respectively her heroic and villainous embodiments. There are several crucial characteristics revealed by speech elements.
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Бережна, Маргарита Василівна. The Traitor Psycholinguistic Archetype. Premier Publishing, 2022. http://dx.doi.org/10.31812/123456789/6051.

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Film studies have recently begun to employ Jung’s concept of archetypes prototypical characters which play the role of blueprint in constructing clear-cut characters. New typologies of archetype characters appear to reflect the changes in the constantly developing world of literature, theater, film, comics and other forms of entertainment. Among those, there is the classification of forty-five master characters by V. Schmidt , which is the basis for defining the character’s archetype in the present article. The aim of the research is to identify the elements of the psycholinguistic image of Justin Hammer in the superhero film Iron Man 2 based on the Marvel Comics and directed by Jon Favreau (2010). The task consists of three stages, namely identification of the psychological characteristics of the character, subsequent determination of Hammer’s archetype and definition of speech elements that reveal the character’s psychological image. This paper explores 92 Hammer’s turns of dialogues in the film. According to V. Schmidt’s classification, Hammer belongs to the Traitor archetype, which is a villainous representation of the Businessman archetype.
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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.
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