Academic literature on the topic 'Image classification tasks'
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Journal articles on the topic "Image classification tasks"
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.
Full textLi, 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.
Full textZhang, 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.
Full textTang, 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.
Full textZhou, 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.
Full textMelekhin, 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.
Full textSingh, 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.
Full textYan, 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.
Full textElizarov, 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.
Full textYan, 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.
Full textDissertations / Theses on the topic "Image classification tasks"
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.
Full textPh.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
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.
Full textSchoening, 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.
Full textMulgrew, 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.
Full textMulgrew, 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/.
Full textMattia, 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/.
Full textBook chapters on the topic "Image classification tasks"
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.
Full textMadrid, 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.
Full textPiras, 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.
Full textBahroun, 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.
Full textSmith, 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.
Full textZenk, 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.
Full textLange, 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.
Full textCandemir, 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.
Full textHashemi, 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.
Full textSoni, 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.
Full textConference papers on the topic "Image classification tasks"
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.
Full textJain, 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.
Full textUbeda, 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.
Full textHuang, 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.
Full textWang, 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.
Full textHe, 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.
Full textChen, 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.
Full textNery, 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.
Full textIzquierdo-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.
Full textLee, 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.
Full textReports on the topic "Image classification tasks"
Бережна, Маргарита Василівна. The Destroyer Psycholinguistic Archetype. Baltija Publishing, 2021. http://dx.doi.org/10.31812/123456789/6036.
Full textБережна, Маргарита Василівна. Maleficent: from the Matriarch to the Scorned Woman (Psycholinguistic Image). Baltija Publishing, 2021. http://dx.doi.org/10.31812/123456789/5766.
Full textБережна, Маргарита Василівна. The Traitor Psycholinguistic Archetype. Premier Publishing, 2022. http://dx.doi.org/10.31812/123456789/6051.
Full textLee, 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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