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Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

Sivasankaran, Deepika, Sai Seena P, Rajesh R, and Madheswari Kanmani. "Sketch Based Image Retrieval using Deep Learning Based Machine Learning." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 79–86. http://dx.doi.org/10.35940/ijeat.e2622.0610521.

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Анотація:

Sketch based image retrieval (SBIR) is a sub-domain of Content Based Image Retrieval(CBIR) where the user provides a drawing as an input to obtain i.e retrieve images relevant to the drawing given. The main challenge in SBIR is the subjectivity of the drawings drawn by the user as it entirely relies on the user's ability to express information in hand-drawn form. Since many of the SBIR models created aim at using singular input sketch and retrieving photos based on the given single sketch input, our project aims to enable detection and extraction of multiple sketches given together as a single input sketch image. The features are extracted from individual sketches obtained using deep learning architectures such as VGG16 , and classified to its type based on supervised machine learning using Support Vector Machines. Based on the class obtained, photos are retrieved from the database using an opencv library, CVLib , which finds the objects present in a photo image. From the number of components obtained in each photo, a ranking function is performed to rank the retrieved photos, which are then displayed to the user starting from the highest order of ranking up to the least. The system consisting of VGG16 and SVM provides 89% accuracy

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2

Reddy, N. Raghu Ram, Gundreddy Suresh Reddy, and Dr M. Narayana. "Color Sketch Based Image Retrieval." International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 03, no. 09 (September 20, 2014): 12179–85. http://dx.doi.org/10.15662/ijareeie.2014.0309054.

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3

Abdul Baqi, Huda Abdulaali, Ghazali Sulong, Siti Zaiton Mohd Hashim, and Zinah S.Abdul jabar. "Innovative Sketch Board Mining for Online image Retrieval." Modern Applied Science 11, no. 3 (November 22, 2016): 13. http://dx.doi.org/10.5539/mas.v11n3p13.

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Анотація:

Developing an accurate and efficient Sketch-Based Image Retrieval (SBIR) method in determining the resemblances between the user's query and image stream has been a never-ending quest in digital data communication era. The main challenge is to overcome the asymmetry between a binary sketch and a full-color image. We introduce a unique sketch board mining method to recover the online web images. This image conceptual retrieval is performed by matching the sketch query with the relevant terminology of selected images. A systematic sequence is followed, including the sketch drawing by the user in interpreting its geometrical shape of the conceptual form based on annotation metadata matching technique achieved automatically from Google engines, indexing and clustering the selected images via data mining. The sketch mining board being built in dynamic drawing state used a set of features to generalize sketch board conceptualization in semantic level. Images from the global repository are retrieved via a semantic match of the user's sketch query with them. Excellent retrieval of hand-drawn sketches is found to achieve the recall rate within 0.1 to 0.8 and a precision rate is 0.7 to 0.98. The proposed technique solved many problems that stat-of-art suffered from SBIR (e.g. scaling, transport, imperfect) sketch. Furthermore, it is demonstrated that the proposed technique allowed us to exploit high-level features to search the web effectively and may constitute a basis for efficient and precise image recovery tool.

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4

Lei, Haopeng, Simin Chen, Mingwen Wang, Xiangjian He, Wenjing Jia, and Sibo Li. "A New Algorithm for Sketch-Based Fashion Image Retrieval Based on Cross-Domain Transformation." Wireless Communications and Mobile Computing 2021 (May 25, 2021): 1–14. http://dx.doi.org/10.1155/2021/5577735.

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Анотація:

Due to the rise of e-commerce platforms, online shopping has become a trend. However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. For huge commodity databases, it remains a long-standing unsolved problem for users to find the interested products quickly. Different from the traditional text-based and exemplar-based image retrieval techniques, sketch-based image retrieval (SBIR) provides a more intuitive and natural way for users to specify their search need. Due to the large cross-domain discrepancy between the free-hand sketch and fashion images, retrieving fashion images by sketches is a significantly challenging task. In this work, we propose a new algorithm for sketch-based fashion image retrieval based on cross-domain transformation. In our approach, the sketch and photo are first transformed into the same domain. Then, the sketch domain similarity and the photo domain similarity are calculated, respectively, and fused to improve the retrieval accuracy of fashion images. Moreover, the existing fashion image datasets mostly contain photos only and rarely contain the sketch-photo pairs. Thus, we contribute a fine-grained sketch-based fashion image retrieval dataset, which includes 36,074 sketch-photo pairs. Specifically, when retrieving on our Fashion Image dataset, the accuracy of our model ranks the correct match at the top-1 which is 96.6%, 92.1%, 91.0%, and 90.5% for clothes, pants, skirts, and shoes, respectively. Extensive experiments conducted on our dataset and two fine-grained instance-level datasets, i.e., QMUL-shoes and QMUL-chairs, show that our model has achieved a better performance than other existing methods.

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5

Saavedra, Jose M., and Benjamin Bustos. "Sketch-based image retrieval using keyshapes." Multimedia Tools and Applications 73, no. 3 (September 7, 2013): 2033–62. http://dx.doi.org/10.1007/s11042-013-1689-0.

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6

Lei, Haopeng, Yugen Yi, Yuhua Li, Guoliang Luo, and Mingwen Wang. "A new clothing image retrieval algorithm based on sketch component segmentation in mobile visual sensors." International Journal of Distributed Sensor Networks 14, no. 11 (November 2018): 155014771881562. http://dx.doi.org/10.1177/1550147718815627.

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Анотація:

Nowadays, the state-of-the-art mobile visual sensors technology makes it easy to collect a great number of clothing images. Accordingly, there is an increasing demand for a new efficient method to retrieve clothing images by using mobile visual sensors. Different from traditional keyword-based and content-based image retrieval techniques, sketch-based image retrieval provides a more intuitive and natural way for users to clarify their search need. However, this is a challenging problem due to the large discrepancy between sketches and images. To tackle this problem, we present a new sketch-based clothing image retrieval algorithm based on sketch component segmentation. The proposed strategy is to first collect a large scale of clothing sketches and images and tag with semantic component labels for training dataset, and then, we employ conditional random field model to train a classifier which is used to segment query sketch into different components. After that, several feature descriptors are fused to describe each component and capture the topological information. Finally, a dynamic component-weighting strategy is established to boost the effect of important components when measuring similarities. The approach is evaluated on a large, real-world clothing image dataset, and experimental results demonstrate the effectiveness and good performance of the proposed method.

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7

IKEDA, TAKASHI, and MASAFUMI HAGIWARA. "CONTENT-BASED IMAGE RETRIEVAL SYSTEM USING NEURAL NETWORKS." International Journal of Neural Systems 10, no. 05 (October 2000): 417–24. http://dx.doi.org/10.1142/s0129065700000326.

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Анотація:

An effective image retrieval system is developed based on the use of neural networks (NNs). It takes advantages of association ability of multilayer NNs as matching engines which calculate similarities between a user's drawn sketch and the stored images. The NNs memorize pixel information of every size-reduced image (thumbnail) in the learning phase. In the retrieval phase, pixel information of a user's drawn rough sketch is inputted to the learned NNs and they estimate the candidates. Thus the system can retrieve candidates quickly and correctly by utilizing the parallelism and association ability of NNs. In addition, the system has learning capability: it can automatically extract features of a user's drawn sketch during the retrieval phase and can store them as additional information to improve the performance. The software for querying, including efficient graphical user interfaces, has been implemented and tested. The effectiveness of the proposed system has been investigated through various experimental tests.

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8

Adimas, Adimas, and Suhendro Y. Irianto. "Image Sketch Based Criminal Face Recognition Using Content Based Image Retrieval." Scientific Journal of Informatics 8, no. 2 (November 30, 2021): 176–82. http://dx.doi.org/10.15294/sji.v8i2.27865.

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Purpose: Face recognition is a geometric space recording activity that allows it to be used to distinguish the features of a face. Therefore, facial recognition can be used to identify ID cards, ATM card PINs, search for one’s committed crimes, terrorists, and other criminals whose faces were not caught by Close-Circuit Television (CCTV). Based on the face image database and by applying the Content-Base Image Retrieval method (CBIR), committed crimes can be recognized on his face. Moreover, the image segmentation technique was carried out before CBIR was applied. This work tried to recognize an individual who committed crimes based on his or her face by using sketch facial images as a query. Methods: We used an image sketch as a querybecause CCTV could not have caught the face image. The research used no less than 1,000 facial images were carried out, both normal as well asabnormal faces (with obstacles). Findings:Experiments demonstrated good enough in terms of precision and recall, which are 0,8 and 0,3 respectively, which is better than at least two previous works.The work demonstrates a precision of 80% which means retrieval of effectiveness is good enough. The 75 queries were carried out in this work to compute the precision and recall of image retrieval. Novelty: Most face recognition researchers using CBIR employed an image as a query. Furthermore, previous work still rarely applied image segmentation as well as CBIR.

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9

Zhang, Xianlin, Xueming Li, Xuewei Li, and Mengling Shen. "Better freehand sketch synthesis for sketch-based image retrieval: Beyond image edges." Neurocomputing 322 (December 2018): 38–46. http://dx.doi.org/10.1016/j.neucom.2018.09.047.

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10

Christanti Mawardi, Viny, Yoferen Yoferen, and Stéphane Bressan. "Sketch-Based Image Retrieval with Histogram of Oriented Gradients and Hierarchical Centroid Methods." E3S Web of Conferences 188 (2020): 00026. http://dx.doi.org/10.1051/e3sconf/202018800026.

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Анотація:

Searching images from digital image dataset can be done using sketch-based image retrieval that performs retrieval based on the similarity between dataset images and sketch image input. Preprocessing is done by using Canny Edge Detection to detect edges of dataset images. Feature extraction will be done using Histogram of Oriented Gradients and Hierarchical Centroid on the sketch image and all the preprocessed dataset images. The features distance between sketch image and all dataset images is calculated by Euclidean Distance. Dataset images used in the test consist of 10 classes. The test results show Histogram of Oriented Gradients, Hierarchical Centroid, and combination of both methods with low and high threshold of 0.05 and 0.5 have average precision and recall values of 90.8 % and 13.45 %, 70 % and 10.64 %, 91.4 % and 13.58 %. The average precision and recall values with low and high threshold of 0.01 and 0.1, 0.3 and 0.7 are 87.2 % and 13.19 %, 86.7 % and 12.57 %. Combination of the Histogram of Oriented Gradients and Hierarchical Centroid methods with low and high threshold of 0.05 and 0.5 produce better retrieval results than using the method individually or using other low and high threshold.

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11

Li, Yi, and Wenzhao Li. "A survey of sketch-based image retrieval." Machine Vision and Applications 29, no. 7 (September 1, 2018): 1083–100. http://dx.doi.org/10.1007/s00138-018-0953-8.

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12

Xu, Yuxin, Yuyao Yan, Yiming Lin, Xi Yang, and Kaizhu Huang. "Sketch Based Image Retrieval for Architecture Images with Siamese Swin Transformer." Journal of Physics: Conference Series 2278, no. 1 (May 1, 2022): 012035. http://dx.doi.org/10.1088/1742-6596/2278/1/012035.

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Abstract Sketch-based image retrieval (SBIR) is an image retrieval task that takes a sketch as input and outputs colour images matching the sketch. Most recent SBIR methods utilise deep learning methods with complicated network designs, which are resource-intensive for practical use. This paper proposes a novel compact framework that takes the siamese network with image view angle information, targeting the SBIR task for architecture images. In particular, the proposed siamese network engages a compact SwinTiny transformer as the backbone encoder. View angle information of the architecture image is fed to the model to further improve search accuracy. To cope with the insufficient sketches issue, simulated building sketches are used in training, which are generated by a pre-trained edge extractor. Experiments show that our model achieves 0.859 top-one accuracy exceeding many baseline models for an architecture retrieval task.

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13

Dutta, Anjan, and Zeynep Akata. "Semantically Tied Paired Cycle Consistency for Any-Shot Sketch-Based Image Retrieval." International Journal of Computer Vision 128, no. 10-11 (July 29, 2020): 2684–703. http://dx.doi.org/10.1007/s11263-020-01350-x.

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Abstract Low-shot sketch-based image retrieval is an emerging task in computer vision, allowing to retrieve natural images relevant to hand-drawn sketch queries that are rarely seen during the training phase. Related prior works either require aligned sketch-image pairs that are costly to obtain or inefficient memory fusion layer for mapping the visual information to a semantic space. In this paper, we address any-shot, i.e. zero-shot and few-shot, sketch-based image retrieval (SBIR) tasks, where we introduce the few-shot setting for SBIR. For solving these tasks, we propose a semantically aligned paired cycle-consistent generative adversarial network (SEM-PCYC) for any-shot SBIR, where each branch of the generative adversarial network maps the visual information from sketch and image to a common semantic space via adversarial training. Each of these branches maintains cycle consistency that only requires supervision at the category level, and avoids the need of aligned sketch-image pairs. A classification criteria on the generators’ outputs ensures the visual to semantic space mapping to be class-specific. Furthermore, we propose to combine textual and hierarchical side information via an auto-encoder that selects discriminating side information within a same end-to-end model. Our results demonstrate a significant boost in any-shot SBIR performance over the state-of-the-art on the extended version of the challenging Sketchy, TU-Berlin and QuickDraw datasets.

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14

Prasad K, Durga, Manjunathachari K, and Giri Prasad M.N. "Orientation Feature Transform Model for Image Retrieval in Sketch Based Image Retrieval System." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 159. http://dx.doi.org/10.14419/ijet.v7i2.24.12022.

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Анотація:

This paper focus on Image retrieval using Sketch based image retrieval system. The low complexity model for image representation has given the sketch based image retrieval (SBIR) a optimal selection for next generation application in low resource environment. The SBIR approach uses the geometrical region representation to describe the feature and utilize for recognition. In the SBIR model, the features represented define the image. Towards the improvement of SBIR recognition performance, in this paper a new invariant modeling using “orientation feature transformed modeling” is proposed. The approach gives the enhancement of invariant property and retrieval performance improvement in transformed domain. The experimental results illustrate the significance of invariant orientation feature representation in SBIR over the conventional models.

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15

More, Prof Rupal D., Rajashri Puranik, Purva Dusane, Sejal Bhawar, and Himanshu Sahu. "Sketch-Based Image Retrieval System for Criminal Records Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 4082–87. http://dx.doi.org/10.22214/ijraset.2023.52585.

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Abstract: An overview to the Sketch Based Image Retrieval for Criminal Record where the user provides a sketch as input to the system to retrieve relevant images from the database. It is seen that traditional methods to draw the face sketch are still difficult and time consuming. This system is developed so that the identification of criminals is done faster than the traditional method. Therefore, the paper presents a simple and effective deep learning framework where user can create the sketch of the suspect and can be matched to the database to get the relevant criminal images. It mainly uses Histogram Oriented Graph, Support Vector Machine, Deep Convolutional Neural Networks machine learning algorithms for face landmarks estimation, feature extraction and pattern matching. SBIR proves to be more efficient and faster to process the criminal face in real-time, as time plays an important role in immediate action in the crime branch.

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16

Ge, Ce, Jingyu Wang, Qi Qi, Haifeng Sun, Tong Xu, and Jianxin Liao. "Scene-Level Sketch-Based Image Retrieval with Minimal Pairwise Supervision." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (June 26, 2023): 650–57. http://dx.doi.org/10.1609/aaai.v37i1.25141.

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Анотація:

The sketch-based image retrieval (SBIR) task has long been researched at the instance level, where both query sketches and candidate images are assumed to contain only one dominant object. This strong assumption constrains its application, especially with the increasingly popular intelligent terminals and human-computer interaction technology. In this work, a more general scene-level SBIR task is explored, where sketches and images can both contain multiple object instances. The new general task is extremely challenging due to several factors: (i) scene-level SBIR inherently shares sketch-specific difficulties with instance-level SBIR (e.g., sparsity, abstractness, and diversity), (ii) the cross-modal similarity is measured between two partially aligned domains (i.e., not all objects in images are drawn in scene sketches), and (iii) besides instance-level visual similarity, a more complex multi-dimensional scene-level feature matching problem is imposed (including appearance, semantics, layout, etc.). Addressing these challenges, a novel Conditional Graph Autoencoder model is proposed to deal with scene-level sketch-images retrieval. More importantly, the model can be trained with only pairwise supervision, which distinguishes our study from others in that elaborate instance-level annotations (for example, bounding boxes) are no longer required. Extensive experiments confirm the ability of our model to robustly retrieve multiple related objects at the scene level and exhibit superior performance beyond strong competitors.

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17

Guo, Yuanchen, Yun Cai, and Songhai Zhang. "Attentive Edgemap Fusion for Sketch-Based Image Retrieval." Journal of Computer-Aided Design & Computer Graphics 33, no. 6 (June 1, 2021): 847–54. http://dx.doi.org/10.3724/sp.j.1089.2021.18589.

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18

Thankachan, Sini. "MindCam: An Approach for Sketch Based Image Retrieval." International Journal of Information Systems and Computer Sciences 8, no. 2 (April 15, 2019): 67–71. http://dx.doi.org/10.30534/ijiscs/2019/16822019.

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19

Ohashi, Gosuke, Yasutake Nagashima, Keita Mochizuki, and Yoshifumi Shimodaira. "Edge-based Image Retrieval Using a Rough Sketch." Journal of the Institute of Image Information and Television Engineers 56, no. 4 (2002): 653–58. http://dx.doi.org/10.3169/itej.56.653.

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20

Wang, Shu, and Zhenjiang Miao. "Sketch-based image retrieval using hierarchical partial matching." Journal of Electronic Imaging 24, no. 4 (August 10, 2015): 043010. http://dx.doi.org/10.1117/1.jei.24.4.043010.

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21

Zhu, Ming, Chun Chen, Nian Wang, Jun Tang, and Wenxia Bao. "Gradually focused fine-grained sketch-based image retrieval." PLOS ONE 14, no. 5 (May 28, 2019): e0217168. http://dx.doi.org/10.1371/journal.pone.0217168.

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22

Wang, Jingyu, Yu Zhao, Qi Qi, Qiming Huo, Jian Zou, Ce Ge, and Jianxin Liao. "MindCamera: Interactive Sketch-Based Image Retrieval and Synthesis." IEEE Access 6 (2018): 3765–73. http://dx.doi.org/10.1109/access.2018.2796638.

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23

Fu, Haiyan, Hanguang Zhao, Xiangwei Kong, and Xianbo Zhang. "BHoG: binary descriptor for sketch-based image retrieval." Multimedia Systems 22, no. 1 (August 9, 2014): 127–36. http://dx.doi.org/10.1007/s00530-014-0406-9.

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24

Zhang, Yuting, Xueming Qian, Xianglong Tan, Junwei Han, and Yuanyan Tang. "Sketch-Based Image Retrieval by Salient Contour Reinforcement." IEEE Transactions on Multimedia 18, no. 8 (August 2016): 1604–15. http://dx.doi.org/10.1109/tmm.2016.2568138.

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25

Sheng, Jianqiang, Fei Wang, Baoquan Zhao, Junkun Jiang, Yu Yang, and Tie Cai. "Sketch-Based Image Retrieval Using Novel Edge Detector and Feature Descriptor." Wireless Communications and Mobile Computing 2022 (February 1, 2022): 1–12. http://dx.doi.org/10.1155/2022/4554911.

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Анотація:

With the explosive increase of digital images, intelligent information retrieval systems have become an indispensable tool to facilitate users’ information seeking process. Although various kinds of techniques like keyword-/content-based methods have been extensively investigated, how to effectively retrieve relevant images from a large-scale database remains a very challenging task. Recently, with the wide availability of touch screen devices and their associated human-computer interaction technology, sketch-based image retrieval (SBIR) methods have attracted more and more attention. In contrast to keyword-based methods, SBIR allows users to flexibly manifest their information needs into sketches by drawing abstract outlines of an object/scene. Despite its ease and intuitiveness, it is still a nontrivial task to accurately extract and interpret the semantic information from sketches, largely because of the diverse drawing styles of different users. As a consequence, the performance of existing SBIR systems is still far from being satisfactory. In this paper, we introduce a novel sketch image edge feature extraction algorithm to tackle the challenges. Firstly, we propose a Gaussian blur-based multiscale edge extraction (GBME) algorithm to capture more comprehensive and detailed features by continuously superimposing the edge filtering results after Gaussian blur processing. Secondly, we devise a hybrid barycentric feature descriptor (RSB-HOG) that extracts HOG features by randomly sampling points on the edges of a sketch. In addition, we integrate the directional distribution of the barycenters of all sampling points into the feature descriptor and thus improve its representational capability in capturing the semantic information of contours. To examine the efficiency of our method, we carry out extensive experiments on the public Flickr15K dataset. The experimental results indicate that the proposed method is superior to existing peer SBIR systems in terms of retrieval accuracy.

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26

R., Dipika, and J. V. "A Sketch based Image Retrieval with Descriptor based on Constraints." International Journal of Computer Applications 146, no. 12 (July 15, 2016): 7–11. http://dx.doi.org/10.5120/ijca2016910923.

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27

Amarnadh, S., P. V. G. D. Reddy, and N. V. E. S. Murthy. "Perlustration on Image Processing under Free Hand Sketch Based Image Retrieval." EAI Endorsed Transactions on Internet of Things 4, no. 16 (October 31, 2018): 159334. http://dx.doi.org/10.4108/eai.21-12-2018.159334.

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28

Ge, Ce, Jingyu Wang, Qi Qi, Haifeng Sun, Tong Xu, and Jianxin Liao. "Semi-transductive Learning for Generalized Zero-Shot Sketch-Based Image Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 7678–86. http://dx.doi.org/10.1609/aaai.v37i6.25931.

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Анотація:

Sketch-based image retrieval (SBIR) is an attractive research area where freehand sketches are used as queries to retrieve relevant images. Existing solutions have advanced the task to the challenging zero-shot setting (ZS-SBIR), where the trained models are tested on new classes without seen data. However, they are prone to overfitting under a realistic scenario when the test data includes both seen and unseen classes. In this paper, we study generalized ZS-SBIR (GZS-SBIR) and propose a novel semi-transductive learning paradigm. Transductive learning is performed on the image modality to explore the potential data distribution within unseen classes, and zero-shot learning is performed on the sketch modality sharing the learned knowledge through a semi-heterogeneous architecture. A hybrid metric learning strategy is proposed to establish semantics-aware ranking property and calibrate the joint embedding space. Extensive experiments are conducted on two large-scale benchmarks and four evaluation metrics. The results show that our method is superior over the state-of-the-art competitors in the challenging GZS-SBIR task.

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29

Liu, Yujie, Changhong Dou, Qilu Zhao, Zongmin Li, and Hua Li. "Sketch Based Image Retrieval with Conditional Generative Adversarial Network." Journal of Computer-Aided Design & Computer Graphics 29, no. 12 (2017): 2336. http://dx.doi.org/10.3724/sp.j.1089.2017.16596.

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30

Torabi Motlagh Fard, Mohammad Hossein, Nazean Jomhari, and Sri Devi Ravana. "Sketch Based Image Retrieval by Using Feature Extraction Technique." Journal of Computer Science & Computational Mathematics 6, no. 1 (March 31, 2016): 21–24. http://dx.doi.org/10.20967/jcscm.2016.01.004.

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31

Wang, Luo, Xueming Qian, Xingjun Zhang, and Xingsong Hou. "Sketch-Based Image Retrieval With Multi-Clustering Re-Ranking." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 12 (December 2020): 4929–43. http://dx.doi.org/10.1109/tcsvt.2019.2959875.

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32

Huang, Fei, Cheng Jin, Yuejie Zhang, Kangnian Weng, Tao Zhang, and Weiguo Fan. "Sketch-based image retrieval with deep visual semantic descriptor." Pattern Recognition 76 (April 2018): 537–48. http://dx.doi.org/10.1016/j.patcog.2017.11.032.

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33

Habrat, Magdalena, and Mariusz Młynarczuk. "Object Retrieval in Microscopic Images of Rocks Using the Query by Sketch Method." Applied Sciences 10, no. 1 (December 30, 2019): 278. http://dx.doi.org/10.3390/app10010278.

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This paper presents the retrieval method of geological images or their fragments using Query by Sketch method. The sketch can be created manually, for instance using a graphics editor, and may show the shape of objects or their distribution within an image. This query is then used to search the image database for objects showing the greatest similarity. As an example of the proposed method, the detection of porosity in microscopic images of carbonate rock and sandstone was presented. An approach was described which is founded on the designation of parameters of selected properties of the query image and images in databases, as well as on the conformity analysis of these parameters. Two methods were proposed: the first one searches for the most similar object in the image database with respect to the set criteria. The second method performs a search based on a sketch of images which are similar in terms of object distribution (i.e., porosity). The presented research results confirm that database search using the query by sketch method forms an interesting and modern approach and may constitute one of the functionalities of IT systems intended for use in geology and mining industry.

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34

Saavedra, Jose M. "RST-SHELO: sketch-based image retrieval using sketch tokens and square root normalization." Multimedia Tools and Applications 76, no. 1 (November 25, 2015): 931–51. http://dx.doi.org/10.1007/s11042-015-3076-5.

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35

Hayashi, Takahiro, Atsushi Ishikawa, and Rikio Onai. "Landscape Image Retrieval with Query by Sketch and Icon." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 1 (January 20, 2007): 61–70. http://dx.doi.org/10.20965/jaciii.2007.p0061.

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This paper reports a new method for retrieving landscape images using a sketch and icons as a query. Based on the proposal, first, a user sketches lines expressing contours of landscape elements such as mountains and forests and attaches icons expressing landscape elements to the sketch. Second, whether individual images in a database match with the layout expressed by the sketch and icons is judged with principal component analysis and pattern recognition. From experimental results, we have confirmed that the proportion of the correct images ranked within top 10 of retrieval results is 80% in an average.

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36

Sabry, Eman S., Salah Elagooz, Fathi E. Abd El-Samie, Walid El-Shafai, Nirmeen A. El-Bahnasawy, Ghada El-Banby, Naglaa F. Soliman, Sudhakar Sengan, and Rabie A. Ramadan. "Sketch-Based Retrieval Approach Using Artificial Intelligence Algorithms for Deep Vision Feature Extraction." Axioms 11, no. 12 (November 22, 2022): 663. http://dx.doi.org/10.3390/axioms11120663.

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Since the onset of civilization, sketches have been used to portray our visual world, and they continue to do so in many different disciplines today. As in specific government agencies, establishing similarities between sketches is a crucial aspect of gathering forensic evidence in crimes, in addition to satisfying the user’s subjective requirements in searching and browsing for specific sorts of images (i.e., clip art images), especially with the proliferation of smartphones with touchscreens. With such a kind of search, quickly and effectively drawing and retrieving sketches from databases can occasionally be challenging, when using keywords or categories. Drawing some simple forms and searching for the image in that way could be simpler in some situations than attempting to put the vision into words, which is not always possible. Modern techniques, such as Content-Based Image Retrieval (CBIR), may offer a more useful solution. The key engine of such techniques that poses various challenges might be dealt with using effective visual feature representation. Object edge feature detectors are commonly used to extract features from different image sorts. However, they are inconvenient as they consume time due to their complexity in computation. In addition, they are complicated to implement with real-time responses. Therefore, assessing and identifying alternative solutions from the vast array of methods is essential. Scale Invariant Feature Transform (SIFT) is a typical solution that has been used by most prevalent research studies. Even for learning-based methods, SIFT is frequently used for comparison and assessment. However, SIFT has several downsides. Hence, this research is directed to the utilization of handcrafted-feature-based Oriented FAST and Rotated BRIEF (ORB) to capture visual features of sketched images to overcome SIFT limitations on small datasets. However, handcrafted-feature-based algorithms are generally unsuitable for large-scale sets of images. Efficient sketched image retrieval is achieved based on content and separation of the features of the black line drawings from the background into precisely-defined variables. Each variable is encoded as a distinct dimension in this disentangled representation. For representation of sketched images, this paper presents a Sketch-Based Image Retrieval (SBIR) system, which uses the information-maximizing GAN (InfoGAN) model. The establishment of such a retrieval system is based on features acquired by the unsupervised learning InfoGAN model to satisfy users’ expectations for large-scale datasets. The challenges with the matching and retrieval systems of such kinds of images develop when drawing clarity declines. Finally, the ORB-based matching system is introduced and compared to the SIFT-based system. Additionally, the InfoGAN-based system is compared with state-of-the-art solutions, including SIFT, ORB, and Convolutional Neural Network (CNN).

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Zhang, Zhaolong, Yuejie Zhang, Rui Feng, Tao Zhang, and Weiguo Fan. "Zero-Shot Sketch-Based Image Retrieval via Graph Convolution Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12943–50. http://dx.doi.org/10.1609/aaai.v34i07.6993.

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Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) has been proposed recently, putting the traditional Sketch-based Image Retrieval (SBIR) under the setting of zero-shot learning. Dealing with both the challenges in SBIR and zero-shot learning makes it become a more difficult task. Previous works mainly focus on utilizing one kind of information, i.e., the visual information or the semantic information. In this paper, we propose a SketchGCN model utilizing the graph convolution network, which simultaneously considers both the visual information and the semantic information. Thus, our model can effectively narrow the domain gap and transfer the knowledge. Furthermore, we generate the semantic information from the visual information using a Conditional Variational Autoencoder rather than only map them back from the visual space to the semantic space, which enhances the generalization ability of our model. Besides, feature loss, classification loss, and semantic loss are introduced to optimize our proposed SketchGCN model. Our model gets a good performance on the challenging Sketchy and TU-Berlin datasets.

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38

Tursun, Osman, Simon Denman, Sridha Sridharan, Ethan Goan, and Clinton Fookes. "An efficient framework for zero-shot sketch-based image retrieval." Pattern Recognition 126 (June 2022): 108528. http://dx.doi.org/10.1016/j.patcog.2022.108528.

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39

Eitz, M., K. Hildebrand, T. Boubekeur, and M. Alexa. "Sketch-Based Image Retrieval: Benchmark and Bag-of-Features Descriptors." IEEE Transactions on Visualization and Computer Graphics 17, no. 11 (November 2011): 1624–36. http://dx.doi.org/10.1109/tvcg.2010.266.

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40

Zhan, Shu, Jingjing Zhao, Yucheng Tang, and Zhenzhu Xie. "Face image retrieval: super-resolution based on sketch-photo transformation." Soft Computing 22, no. 4 (November 8, 2016): 1351–60. http://dx.doi.org/10.1007/s00500-016-2427-0.

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41

Wang, Luo, Xueming Qian, Yuting Zhang, Jialie Shen, and Xiaochun Cao. "Enhancing Sketch-Based Image Retrieval by CNN Semantic Re-ranking." IEEE Transactions on Cybernetics 50, no. 7 (July 2020): 3330–42. http://dx.doi.org/10.1109/tcyb.2019.2894498.

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42

Pillay, Karan Ravindran, and Omkar Upendra Khadilkar. "The Scalable Image Retrieval Systems and Applications." International Journal of Engineering and Computer Science 7, ``11 (November 13, 2018): 24406–8. http://dx.doi.org/10.18535/ijecs/v7i11.03.

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Анотація:

Advances in information storage and image acquisition technologies have enabled the creation of enormous image datasets. during this situation, it's necessary to develop applicable data systems to with efficiency manage these collections. the most typical approaches use the supposed Content-Based Image Retrieval (CBIR) systems. Basically, these systems attempt to retrieve pictures like a user-defined specification or pattern (e.g., form sketch, image example). Their goal is to support image retrieval supported content properties (e.g., shape, color, texture), typically encoded into feature vectors. one among the most benefits of the CBIR approach is that the chance of AN automatic retrieval method, rather than the standard keyword-based approach, thattypically needs terribly toilsome and long previous annotation of info pictures. The CBIR technology has been utilized in many applications like fingerprint identification, variety data systems, digital libraries, crime bar, medicine, historical analysis, among others.

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Jhansi, Y., and E. Sreenivasa Reddy. "A Methodology for Sketch based Image Retrieval based on Score level Fusion." International Journal of Computer Applications 109, no. 3 (January 16, 2015): 9–13. http://dx.doi.org/10.5120/19167-0629.

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44

Chaudhuri, Ushasi, Biplab Banerjee, Avik Bhattacharya, and Mihai Datcu. "CrossATNet - a novel cross-attention based framework for sketch-based image retrieval." Image and Vision Computing 104 (December 2020): 104003. http://dx.doi.org/10.1016/j.imavis.2020.104003.

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45

Li, Jiangtong, Zhixin Ling, Li Niu, and Liqing Zhang. "Zero-shot sketch-based image retrieval with structure-aware asymmetric disentanglement." Computer Vision and Image Understanding 218 (April 2022): 103412. http://dx.doi.org/10.1016/j.cviu.2022.103412.

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46

Cai, Jia, Guanglong Xu, and Zhensheng Hu. "Sketch-based image retrieval via CAT loss with elastic net regularization." Mathematical Foundations of Computing 3, no. 4 (2020): 219–27. http://dx.doi.org/10.3934/mfc.2020013.

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47

Qian, Xueming, Xianglong Tan, Yuting Zhang, Richang Hong, and Meng Wang. "Enhancing Sketch-Based Image Retrieval by Re-Ranking and Relevance Feedback." IEEE Transactions on Image Processing 25, no. 1 (January 2016): 195–208. http://dx.doi.org/10.1109/tip.2015.2497145.

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48

Jhansi, Y. "Sketch-based Image Retrieval using Rotation-invariant Histograms of Oriented Gradients." International Journal of Computer Trends and Technology 49, no. 2 (July 25, 2017): 121–24. http://dx.doi.org/10.14445/22312803/ijctt-v49p118.

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49

Rajput, G. G., and Prashantha. "Sketch based image retrieval using grid approach on large scale database." Procedia Computer Science 165 (2019): 216–23. http://dx.doi.org/10.1016/j.procs.2020.01.089.

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50

Di Sciascio, E., F. M. Donini, and M. Mongiello. "Spatial layout representation for query-by-sketch content-based image retrieval." Pattern Recognition Letters 23, no. 13 (November 2002): 1599–612. http://dx.doi.org/10.1016/s0167-8655(02)00124-1.

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