Relevant bibliographies by topics / Sketch-based Image retrieval

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  1. Journal articles
  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Sketch-based Image retrieval'

Author: Grafiati
Published: 6 September 2023
Last updated: 31 July 2025

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

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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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Dissertations / Theses on the topic "Sketch-based Image retrieval"

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Saavedra, Rondo José Manuel. "Image Descriptions for Sketch Based Image Retrieval." Tesis, Universidad de Chile, 2013. http://www.repositorio.uchile.cl/handle/2250/112670.

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Doctor en Ciencias, Mención Computación<br>Debido al uso masivo de Internet y a la proliferación de dispositivos capaces de generar información multimedia, la búsqueda y recuperación de imágenes basada en contenido se han convertido en áreas de investigación activas en ciencias de la computación. Sin embargo, la aplicación de búsqueda por contenido requiere una imagen de ejemplo como consulta, lo cual muchas veces puede ser un problema serio, que imposibilite la usabilidad de la aplicación. En efecto, los usuarios comúnmente hacen uso de un buscador de imágenes porque no cuentan con la imagen
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Dey, Sounak. "Mapping between Images and Conceptual Spaces: Sketch-based Image Retrieval." Doctoral thesis, Universitat Autònoma de Barcelona, 2020. http://hdl.handle.net/10803/671082.

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El diluvi de contingut visual a Internet –de contingut generat per l’usuari a col·leccions d’imatges comercials- motiva nous mètodes intuïtius per cercar contingut d’imatges digitals: com podem trobar determinades imatges en una base de dades de milions? La recuperació d’imatges basada en esbossos (SBIR) és un tema de recerca emergent en què es pot utilitzar un dibuix a mà lliure per consultar visualment imatges fotogràfiques. SBIR s’alinea a les tendències emergents de consum de contingut visual en dispositius mòbils basats en pantalla tàctil, per a les quals les interaccions gestuals com el
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Bui, Tu. "Sketch based image retrieval on big visual data." Thesis, University of Surrey, 2019. http://epubs.surrey.ac.uk/850099/.

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The deluge of visual content on the Internet - from user-generated content to commercial image collections - motivates intuitive new methods for searching digital image content: how can we find certain images in a database of millions? Sketch-based image retrieval (SBIR) is an emerging research topic in which a free-hand drawing can be used to visually query photographic images. SBIR is aligned to emerging trends for visual content consumption on mobile touch-screen based devices, for which gestural interactions such as sketch are a natural alternative to textual input. This thesis presents se
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Kamvysselis, Manolis 1977, and Ovidiu 1975 Marina. "Imagina : a cognitive abstraction approach to sketch-based image retrieval." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/16724.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.<br>Includes bibliographical references (leaves 151-157).<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>As digital media become more popular, corporations and individuals gather an increasingly large number of digital images. As a collection grows to more than a few hundred images, the need for search becomes crucial. This thesis is addressing the problem of retrievi
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Tseng, Kai-Yu, and 曾開瑜. "Sketch-based Image Retrieval on Mobile Devices Using Compact Hash Bits." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/86812733175523374451.

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碩士<br>國立臺灣大學<br>資訊網路與多媒體研究所<br>100<br>With the advance of science and technology, touch panels in mobile devices has provided a good platform for mobile sketch search. Moreover, the request of real time application on mobile devices becomes increasingly urgent and most applications are based on large dataset so these dataset should be indexed for efficiency. However, most of previous sketch image retrieval system are usually provided on the server side and simply adopt an inverted index structure on image database, which is formidable to be operated in the limited memory of mobile devices inde
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Liu, Ching-Hsuan, and 劉璟萱. "Exploiting Word and Visual Word Co-occurrence for Sketch-based Image Retrieval." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/20273244983524928076.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>103<br>As the increasing popularity of touch-screen devices, retrieving images by hand-drawn sketch has become a trend. Human sketch can easily express some complex user intention such as the object shape. However, sketches are sometimes ambiguous due to different drawing styles and inter-class object shape ambiguity. Although adding text queries as semantic information can help removing the ambiguity of sketch, it requires a huge amount of efforts to annotate text tags to all database images. We propose a method directly model the relationship between text and imag
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Dutta, Titir. "Generalizing Cross-domain Retrieval Algorithms." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5869.

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Cross-domain retrieval is an important research topic due to its wide range of applications in e-commerce, forensics etc. It addresses the data retrieval problem from a search set, when the query belongs to one domain, and the search database contains samples from some other domain. Several algorithms have been proposed for the same in recent literature to address this task. In this thesis, we address some of the challenges in cross-domain retrieval, specifically for the application of sketch-based image retrieval. Traditionally, cross-domain algorithms assume that both the training and tes
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Book chapters on the topic "Sketch-based Image retrieval"

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Xia, Yu, Shuangbu Wang, Yanran Li, Lihua You, Xiaosong Yang, and Jian Jun Zhang. "Fine-Grained Color Sketch-Based Image Retrieval." In Advances in Computer Graphics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22514-8_40.

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Bhunia, Ayan Kumar, Aneeshan Sain, Parth Hiren Shah, et al. "Adaptive Fine-Grained Sketch-Based Image Retrieval." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19836-6_10.

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Sharath Kumar, Y. H., and N. Pavithra. "KD-Tree Approach in Sketch Based Image Retrieval." In Mining Intelligence and Knowledge Exploration. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26832-3_24.

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Birari, Dipika, Dilendra Hiran, and Vaibhav Narawade. "Survey on Sketch Based Image and Data Retrieval." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8715-9_34.

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Zhang, Xiao, and Xuejin Chen. "Robust Sketch-Based Image Retrieval by Saliency Detection." In MultiMedia Modeling. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27671-7_43.

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Shi, Yufeng, Xinge You, Wenjie Wang, Feng Zheng, Qinmu Peng, and Shuo Wang. "Retrieval by Classification: Discriminative Binary Embedding for Sketch-Based Image Retrieval." In Pattern Recognition and Computer Vision. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31726-3_2.

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Bozas, Konstantinos, and Ebroul Izquierdo. "Large Scale Sketch Based Image Retrieval Using Patch Hashing." In Advances in Visual Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33179-4_21.

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Parui, Sarthak, and Anurag Mittal. "Similarity-Invariant Sketch-Based Image Retrieval in Large Databases." In Computer Vision – ECCV 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10599-4_26.

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Wang, Tianqi, Liyan Zhang, and Jinhui Tang. "Sketch-Based Image Retrieval with Multiple Binary HoG Descriptor." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8530-7_4.

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Wu, Xinhui, and Shuangjiu Xiao. "Sketch-Based Image Retrieval via Compact Binary Codes Learning." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04224-0_25.

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Conference papers on the topic "Sketch-based Image retrieval"

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Koley, Subhadeep, Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, and Yi-Zhe Song. "How to Handle Sketch-Abstraction in Sketch-Based Image Retrieval?" In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01595.

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Wang, Shu, and Zhenjiang Miao. "Sketch-based image retrieval using sketch tokens." In 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR). IEEE, 2015. http://dx.doi.org/10.1109/acpr.2015.7486533.

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Wang, Zhipeng, Hao Wang, Jiexi Yan, Aming Wu, and Cheng Deng. "Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/158.

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Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a novel cross-modal retrieval task, where abstract sketches are used as queries to retrieve natural images under zero-shot scenario. Most existing methods regard ZS-SBIR as a traditional classification problem and employ a cross-entropy or triplet-based loss to achieve retrieval, which neglect the problems of the domain gap between sketches and natural images and the large intra-class diversity in sketches. Toward this end, we propose a novel Domain-Smoothing Network (DSN) for ZS-SBIR. Specifically, a cross-modal contrastive method is propose
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Li, Yunfei, and Xiaojing Liu. "Sketch Based Thangka Image Retrieval." In 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2021. http://dx.doi.org/10.1109/iaeac50856.2021.9390657.

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Kondo, Shin-ichiro, Masahiro Toyoura, and Xiaoyang Mao. "Sketch based skirt image retrieval." In the 4th Joint Symposium. ACM Press, 2014. http://dx.doi.org/10.1145/2630407.2630410.

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Jiang, Tianbi, Gui-Song Xia, and Qikai Lu. "Sketch-based aerial image retrieval." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296971.

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Zuo, Ran, Haoxiang Hu, Xiaoming Deng, et al. "SceneDiff: Generative Scene-Level Image Retrieval with Text and Sketch Using Diffusion Models." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/202.

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Jointly using text and sketch for scene-level image retrieval utilizes the complementary between text and sketch to describe the fine-grained scene content and retrieve the target image, which plays a pivotal role in accurate image retrieval. Existing methods directly fuse the features of sketch and text and thus suffer from the bottleneck of limited utilization for crucial semantic and structural information, leading to inaccurate matching with images. In this paper, we propose SceneDiff, a novel retrieval network that leverages a pre-trained diffusion model to establish a shared generative l
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Matsui, Yusuke, Kiyoharu Aizawa, and Yushi Jing. "Sketch2Manga: Sketch-based manga retrieval." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025626.

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Chaudhuri, Abhra, Ayan Kumar Bhunia, Yi-Zhe Song, and Anjan Dutta. "Data-Free Sketch-Based Image Retrieval." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.01163.

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Liu, Li, Fumin Shen, Yuming Shen, Xianglong Liu, and Ling Shao. "Deep Sketch Hashing: Fast Free-Hand Sketch-Based Image Retrieval." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.247.

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