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Semi-supervised Hashing with Semantic Confidence for Large Scale Visual Search

Yingwei Pan, Ting Yao, Houqiang Li, Chong-Wah Ngo, Tao Mei
2015 Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15  
Similarity search is one of the fundamental problems for large scale multimedia applications.  ...  In this paper, we propose a novel semi-supervised hashing framework by leveraging semantic confidence.  ...  the semi-supervised hashing with semantic confidence.  ... 
doi:10.1145/2766462.2767725 dblp:conf/sigir/PanYLNM15 fatcat:4p2rycmghjgflcuybwe67fey5y

A Survey on Deep Hashing Methods [article]

Xiao Luo, Haixin Wang, Daqing Wu, Chong Chen, Minghua Deng, Jianqiang Huang, Xian-Sheng Hua
2022 arXiv   pre-print
We also introduce three related important topics including semi-supervised deep hashing, domain adaption deep hashing and multi-modal deep hashing.  ...  Hashing is one of the most widely used methods for its computational and storage efficiency. With the development of deep learning, deep hashing methods show more advantages than traditional methods.  ...  We also thank Zeyu Ma, Huasong Zhong and Xiaokang Chen who discussed with us and provided instructive suggestions.  ... 
arXiv:2003.03369v5 fatcat:m2iu3htilvgztkcazw3cyk6iqe

Web Image Retrieval using Hashing Technique: A Survey

Dattatray Waghole, Rupesh Bagul, Sandeep Turai, Ashwini Dawlekar
2015 International Journal of Engineering Research and  
 Abstract-Web Image retrieval, is an effective approach to regain the effective results for image searched by the users with the help of queries.  ...  To overcome the above challenge, the semantic signatures proposed for the effective output in the web images. But it fails to produce the matching efficiencies in the results.  ...  [2] Large Web scale image exploration engines habitually user use keywords or queries as the input and relies on immediate content to search images.  ... 
doi:10.17577/ijertv4is020855 fatcat:4hlbcnwllnb4zeqescf7odq7vy

Two Birds, One Stone: Jointly Learning Binary Code for Large-Scale Face Image Retrieval and Attributes Prediction

Yan Li, Ruiping Wang, Haomiao Liu, Huajie Jiang, Shiguang Shan, Xilin Chen
2015 2015 IEEE International Conference on Computer Vision (ICCV)  
We address the challenging large-scale content-based face image retrieval problem, intended as searching images based on the presence of specific subject, given one face image of him/her.  ...  To evaluate the effectiveness of the proposed method, extensive experiments are conducted on a new purified large-scale web celebrity database, named CFW 60K, with abundant manual identity and attributes  ...  Representative methods include Semi-Supervised Hashing (SSH) [34] , Kernel-based Supervised Hashing (KSH) [21] , Discriminative Binary Code (DBC) [26] , and Supervised ITerative Quantization (SITQ)  ... 
doi:10.1109/iccv.2015.435 dblp:conf/iccv/LiWLJSC15 fatcat:hbbxbwtjsnaotkbvbacbqsyofe

A Review of Hashing Methods for Multimodal Retrieval

Wenming Cao, Wenshuo Feng, Qiubin Lin, Guitao Cao, Zhihai He
2020 IEEE Access  
For more information, see http://creativecommons.org/licenses/by/4.0/  ...  This review clarifies the definition of multimodal retrieval requirements and some related concepts, then introduces some representative hashing methods, mainly supervised methods that make full use of  ...  Representatives are Binary Reconstructive Embedding (BRE) [30] , Supervised Hashing with Kernels (KSH) [31] , Kernel Hyper-plane Learning Semi-supervised Hashing (KHLSSH) [32] , and Semantic Confidence  ... 
doi:10.1109/access.2020.2968154 fatcat:e3vmte5hrnhu3b3lf5ws4gwnhm

A Survey on Deep Hashing Methods

Xiao Luo, Haixin Wang, Daqing Wu, Chong Chen, Minghua Deng, Jianqiang Huang, Xian-Sheng Hua
2022 ACM Transactions on Knowledge Discovery from Data  
We also introduce three related important topics including semi-supervised deep hashing, domain adaption deep hashing and multi-modal deep hashing.  ...  Hashing is one of the most widely used methods for its computational and storage efficiency. With the development of deep learning, deep hashing methods show more advantages than traditional methods.  ...  We also thank Zeyu Ma, Huasong Zhong and Xiaokang Chen who discussed with us and provided instructive suggestions.  ... 
doi:10.1145/3532624 fatcat:7lxtu2qzvvhrpnjngefli2mvca

State of the Art: A Summary of Semantic Image and Video Retrieval Techniques

S. Suguna, C. Ranjith Kumar, D. Sheela Jeyarani
2015 Indian Journal of Science and Technology  
As there is developments in all fields, media becomes more popular and so people begun to search videos to know the world happenings visually.  ...  Due to these reasons semantic video retrieval became a challenging issue in various industries.  ...  ., mapped Visual feature into binary codes for large scale image retrieval. In 8 , local feature based on image retrieval where each feature is indexed by hash table.  ... 
doi:10.17485/ijst/2015/v8i35/77061 fatcat:2htopyojqjd7bkjt6mx66cf24i

Semi-supervised learning for scalable and robust visual search

Jun Wang
2011 ACM SIGMultimedia Records  
Semi-Supervised Learning for Scalable and Robust Visual Search Jun Wang Unlike textual document retrieval, searching of visual data is still far from satisfactory.  ...  The synergistic combination of the two offers great potential for advancing the state-of-the-art in large-scale visual search and many other applications.  ...  Large-Scale Visual Indexing and Search: Finally, we apply the proposed semi-supervised hashing techniques [158] [159] to indexing and searching large-scale image databases, including a Flickr image  ... 
doi:10.1145/2069210.2069213 fatcat:hblb5ncrprcrlgi6ugph6naucy

Dual local consistency hashing with discriminative projections selection

Peng Li, Jian Cheng, Hanqing Lu
2013 Signal Processing  
Semantic hashing is a promising way to accelerate similarity search, which designs compact binary codes for a large number of images so that semantically similar images are mapped to close codes.  ...  Therefore, the binary codes learned by our approach are more powerful and discriminative for similarity search.  ...  Over the past decades, several Approximate Nearest Neighbor (ANN) search techniques have been developed for large scale applications.  ... 
doi:10.1016/j.sigpro.2012.05.035 fatcat:nor3zecct5hoheeorqkxhih6d4

Non-transitive Hashing with Latent Similarity Components

Mingdong Ou, Peng Cui, Fei Wang, Jun Wang, Wenwu Zhu
2015 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15  
Approximating the semantic similarity between entities in the learned Hamming space is the key for supervised hashing techniques.  ...  For example, in social networks, we connect with people for various reasons, such as sharing common interests, working in the same company, being alumni and so on.  ...  INTRODUCTION With the explosive growth of data, similarity search is becoming increasingly important for a wide range of large scale applications, including image retrieval [9, 21] , document search  ... 
doi:10.1145/2783258.2783283 dblp:conf/kdd/OuCWW015 fatcat:j65aad6s7vhireephoqozl3ma4

Deep Progressive Hashing for Image Retrieval

Jiale Bai, Bingbing Ni, Minsi Wang, Yang Shen, Hanjiang Lai, Chongyang Zhang, Lin Mei, Chuanping Hu, Chen Yao
2017 Proceedings of the 2017 ACM on Multimedia Conference - MM '17  
This paper proposes a novel recursive hashing scheme, in contrast to conventional one-off based hashing algorithms.  ...  The proposed deep hashing network is trained via minimizing a triplet ranking loss, which is end-to-end trainable.  ...  Representing the image with binary codes (known as Hashing [5, 6, 16, 24, 26] ), is an efficient method for image search, which maps images with similar semantic information to binary codes with small  ... 
doi:10.1145/3123266.3123280 dblp:conf/mm/BaiNWSLZMHY17 fatcat:zmcavxe6bzcu5dtr4kkyqetnxu

Robust discrete code modeling for supervised hashing

Yadan Luo, Yang Yang, Fumin Shen, Zi Huang, Pan Zhou, Heng Tao Shen
2018 Pattern Recognition  
Highlights • We propose a novel supervised hashing scheme to generate high-quality hash codes and hash functions for facilitating large-scale multimedia applications. • We devise an effective binary code  ...  Particularly, with supervision knowledge (e.g., semantic labels), we may further gain considerable performance boost.  ...  Lang, Multiple feature kernel hashing for large-scale visual search, Pattern Recognition 47 (2) (2014) 748-757. [28] C. Deng, X. Liu, Y. Mu, J.  ... 
doi:10.1016/j.patcog.2017.02.034 fatcat:5n2xtxvg7jh7pofrbcahv33ofm

Recent Advance in Content-based Image Retrieval: A Literature Survey [article]

Wengang Zhou, Houqiang Li, Qi Tian
2017 arXiv   pre-print
With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content.  ...  We conclude with several promising directions for future research.  ...  In literature, there are many hashing algorithms for approximate nearest neighbor (ANN) search, such as LSH [140] [141], multi-probe LSH [142] , kernelized LSH [56] , semi-supervised hashing method  ... 
arXiv:1706.06064v2 fatcat:m52xwsw5pzfzdbxo5o6dye2gde

Attribute Discovery via Predictable Discriminative Binary Codes [chapter]

Mohammad Rastegari, Ali Farhadi, David Forsyth
2012 Lecture Notes in Computer Science  
We also evaluate our method on ImageNet and show that our method outperforms state-of-the-art binary code methods on this large scale dataset.  ...  Category memberships are usually good proxies for visual similarity but should not be enforced as a hard constraint.  ...  Semantic hashing methods can produce very efficient image search methods for collections of millions of images [8] .  ... 
doi:10.1007/978-3-642-33783-3_63 fatcat:ztc7zbgc5varrcoiswfwcrrydu

Efficient large-scale image annotation by probabilistic collaborative multi-label propagation

Xiangyu Chen, Yadong Mu, Shuicheng Yan, Tat-Seng Chua
2010 Proceedings of the international conference on Multimedia - MM '10  
Annotating large-scale image corpus requires huge amount of human efforts and is thus generally unaffordable, which directly motivates recent development of semi-supervised or active annotation methods  ...  is efficiently derived with Locality Sensitive Hashing approach followed by sparse 1-graph construction within the individual hashing buckets. 3) An efficient and convergency provable iterative procedure  ...  Graph-based semi-supervised learning with multiple labels. Journal of Visual Communication and Image Representation, 20(2):97-103, February 2009. [23] X. Zhu. Semi-supervised learning with graphs.  ... 
doi:10.1145/1873951.1873959 dblp:conf/mm/ChenMYC10 fatcat:rq47wp4mwzhwtazgzvtu27tvsy
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