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Journal articles on the topic 'Sketch-based Image retrieval'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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1

Dhole, Trupti, Urmila Shelake, Sagar Surwase, Preetam Joshi, and Dhananjay Bhosale. "Survey on Sketch based Image Retrieval." International Journal of Scientific Engineering and Research 4, no. 10 (2016): 46–49. https://doi.org/10.70729/ijser15979.

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Deepika, Sivasankaran, Seena P. Sai, R. Rajesh, and Kanmani Madheswari. "Sketch Based Image Retrieval using Deep Learning Based Machine Learning." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 5 (2021): 79–86. https://doi.org/10.35940/ijeat.E2622.0610521.

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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 si

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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 (2021): 79–86. http://dx.doi.org/10.35940/ijeat.e2622.0610521.

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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

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Luo, Qing, Xiang Gao, Bo Jiang, Xueting Yan, Wanyuan Liu, and Junchao Ge. "A review of fine-grained sketch image retrieval based on deep learning." Mathematical Biosciences and Engineering 20, no. 12 (2023): 21186–210. http://dx.doi.org/10.3934/mbe.2023937.

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<abstract> <p>Sketch image retrieval is an important branch of the image retrieval field, mainly relying on sketch images as queries for content search. The acquisition process of sketch images is relatively simple and in some scenarios, such as when it is impossible to obtain photos of real objects, it demonstrates its unique practical application value, attracting the attention of many researchers. Furthermore, traditional generalized sketch image retrieval has its limitations when it comes to practical applications; merely retrieving images from the same category may not adequat

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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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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 fashi

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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 (2016): 13. http://dx.doi.org/10.5539/mas.v11n3p13.

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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

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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 (2014): 12179–85. http://dx.doi.org/10.15662/ijareeie.2014.0309054.

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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 (2018): 155014771881562. http://dx.doi.org/10.1177/1550147718815627.

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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-bas

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

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

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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 ab

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Gatti, Prajwal, Kshitij Parikh, Dhriti Prasanna Paul, Manish Gupta, and Anand Mishra. "Composite Sketch+Text Queries for Retrieving Objects with Elusive Names and Complex Interactions." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (2024): 1869–77. http://dx.doi.org/10.1609/aaai.v38i3.27956.

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Non-native speakers with limited vocabulary often struggle to name specific objects despite being able to visualize them, e.g., people outside Australia searching for ‘numbats.’ Further, users may want to search for such elusive objects with difficult-to-sketch interactions, e.g., “numbat digging in the ground.” In such common but complex situations, users desire a search interface that accepts composite multimodal queries comprising hand-drawn sketches of “difficult-to-name but easy-to-draw” objects and text describing “difficult-to-sketch but easy-to-verbalize” object's attributes or interac

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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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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 res

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Kaur, Bhupinder. "A Deep Learning Approach for Content-Based Image Retrieval." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50977.

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Content-Based Image Retrieval (CBIR) aims to retrieve relevant images based on visual content rather than metadata, addressing the limitations of traditional retrieval methods. This study proposes a deep learning-based CBIR system utilizing Convolutional Neural Networks (CNNs) for automatic feature extraction. Leveraging the CIFAR-10 dataset, the system is evaluated against traditional handcrafted methods such as color histograms and color moments. Various retrieval paradigms image-based, text-based, sketch-based, and conceptual layout are analyzed for performance comparison. Experimental resu

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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 (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 ima

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Yang, Bo, Chen Wang, Xiaoshuang Ma, Beiping Song, Zhuang Liu, and Fangde Sun. "Zero-Shot Sketch-Based Remote-Sensing Image Retrieval Based on Multi-Level and Attention-Guided Tokenization." Remote Sensing 16, no. 10 (2024): 1653. http://dx.doi.org/10.3390/rs16101653.

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Effectively and efficiently retrieving images from remote-sensing databases is a critical challenge in the realm of remote-sensing big data. Utilizing hand-drawn sketches as retrieval inputs offers intuitive and user-friendly advantages, yet the potential of multi-level feature integration from sketches remains underexplored, leading to suboptimal retrieval performance. To address this gap, our study introduces a novel zero-shot, sketch-based retrieval method for remote-sensing images, leveraging multi-level feature extraction, self-attention-guided tokenization and filtering, and cross-modali

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Gopu, Venkata Rama Muni Kumar, and Madhavi Dunna. "Zero-Shot Sketch-Based Image Retrieval Using StyleGen and Stacked Siamese Neural Networks." Journal of Imaging 10, no. 4 (2024): 79. http://dx.doi.org/10.3390/jimaging10040079.

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Sketch-based image retrieval (SBIR) refers to a sub-class of content-based image retrieval problems where the input queries are ambiguous sketches and the retrieval repository is a database of natural images. In the zero-shot setup of SBIR, the query sketches are drawn from classes that do not match any of those that were used in model building. The SBIR task is extremely challenging as it is a cross-domain retrieval problem, unlike content-based image retrieval problems because sketches and images have a huge domain gap. In this work, we propose an elegant retrieval methodology, StyleGen, for

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Adimas, Adimas, and Suhendro Y. Irianto. "Image Sketch Based Criminal Face Recognition Using Content Based Image Retrieval." Scientific Journal of Informatics 8, no. 2 (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 a

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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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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 (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 ali

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Chandresh, Pratap Singh. "R-Tree Implementation of Image Databases." Signal & Image Processing : An International Journal (SIPIJ) 2, no. 4 (2019): 89–108. https://doi.org/10.5281/zenodo.3501853.

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With the onslaught of multimedia in the near past, there has been a tremendous increase in the uses of images. A very good example of which is the web on which most of the documents contain images. Other than this the images are being used in other applications like weather forecasting, medical diagnosis, police department. In R-Tree implementation of image database, images are made available to the program which are then stores in the database. The image database is presented using R-tree and the database is stored in separate file .This R-tree implementation results in both update as well as

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

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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 (2018): 159. http://dx.doi.org/10.14419/ijet.v7i2.24.12022.

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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 ap

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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 (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 use

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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 (2023): 650–57. http://dx.doi.org/10.1609/aaai.v37i1.25141.

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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-spec

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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 (2021): 847–54. http://dx.doi.org/10.3724/sp.j.1089.2021.18589.

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

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

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

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

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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 (2016): 1604–15. http://dx.doi.org/10.1109/tmm.2016.2568138.

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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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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 contr

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Habrat, Magdalena, and Mariusz Młynarczuk. "Object Retrieval in Microscopic Images of Rocks Using the Query by Sketch Method." Applied Sciences 10, no. 1 (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

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

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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 (2023): 7678–86. http://dx.doi.org/10.1609/aaai.v37i6.25931.

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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 expl

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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 (2018): 159334. http://dx.doi.org/10.4108/eai.21-12-2018.159334.

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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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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 (2016): 21–24. http://dx.doi.org/10.20967/jcscm.2016.01.004.

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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 (2020): 4929–43. http://dx.doi.org/10.1109/tcsvt.2019.2959875.

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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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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 (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

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

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Sabry, Eman S., Salah Elagooz, Fathi E. Abd El-Samie, et al. "Sketch-Based Retrieval Approach Using Artificial Intelligence Algorithms for Deep Vision Feature Extraction." Axioms 11, no. 12 (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 c

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

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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 vec

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Pavithra, Narasimha Murthy, and Kumar Yeliyur Hanumanthaiah Sharath. "Novel hybrid generative adversarial network for synthesizing image from sketch." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6293–301. https://doi.org/10.11591/ijece.v13i6.pp6293-6301.

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In the area of sketch-based image retrieval process, there is a potential difference between retrieving the match images from defined dataset and constructing the synthesized image. The former process is quite easier while the latter process requires more faster, accurate, and intellectual decision making by the processor. After reviewing open-end research problems from existing approaches, the proposed scheme introduces a computational framework of hybrid generative adversarial network (GAN) as a solution to address the identified research problem. The model takes the input of query image whi

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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 (2020): 12943–50. http://dx.doi.org/10.1609/aaai.v34i07.6993.

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Abstract:

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 e

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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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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 (2011): 1624–36. http://dx.doi.org/10.1109/tvcg.2010.266.

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

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