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Feature hashing

About: Feature hashing is a research topic. Over the lifetime, 993 publications have been published within this topic receiving 51462 citations.


Papers
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Journal ArticleDOI
TL;DR: GLOCAL image hashing method utilizing the hierarchical histogram which is based on histogram bin population method is proposed, which can raise the magnitude of hash string generated from same context or features and also strengthen the robustness of generated hash.
Abstract: Recently, web applications, such as Stock Image and Image Library, are developed to provide the integrated management for user's images. Image hashing techniques are used for the image registration, management and retrieval as the identifier also, investigations have been performed to raise the hash performance like discernment. This paper proposes GLOCAL image hashing method utilizing the hierarchical histogram which is based on histogram bin population method. So far, many studies have proven that image hashing techniques based on this histogram are robust against image processing and geometrical attacks. We modified existing image hashing method developed by our research team [20]. The main idea of the paper is that it helps generate more fluent hash string if we have specific length of histogram bin. Another operation is empowering weighting factor into hash string at each level. Thus, we can raise the magnitude of hash string generated from same context or features and also strengthen the robustness of generated hash.

39 citations

Book ChapterDOI
06 Sep 2014
TL;DR: A novel semi-supervised tag hashing (SSTH) approach that fully incorporates tag information into learning effective hashing function by exploring the correlation between tags and hashing bits and improves the effectiveness of hashing function through orthogonal transformation by minimizing the quantization error.
Abstract: Similarity search is an important technique in many large scale vision applications. Hashing approach becomes popular for similarity search due to its computational and memory efficiency. Recently, it has been shown that the hashing quality could be improved by combining supervised information, e.g. semantic tags/labels, into hashing function learning. However, tag information is not fully exploited in existing unsupervised and supervised hashing methods especially when only partial tags are available. This paper proposes a novel semi-supervised tag hashing (SSTH) approach that fully incorporates tag information into learning effective hashing function by exploring the correlation between tags and hashing bits. The hashing function is learned in a unified learning framework by simultaneously ensuring the tag consistency and preserving the similarities between image examples. An iterative coordinate descent algorithm is designed as the optimization procedure. Furthermore, we improve the effectiveness of hashing function through orthogonal transformation by minimizing the quantization error. Extensive experiments on two large scale image datasets demonstrate the superior performance of the proposed approach over several state-of-the-art hashing methods.

39 citations

Proceedings ArticleDOI
03 Nov 2014
TL;DR: This work proposes a cross-media hashing approach based on multi-modal neural networks that achieves superior cross- media retrieval performance compared with the state-of-the-art methods.
Abstract: Cross-media hashing, which conducts cross-media retrieval by embedding data from different modalities into a common low-dimensional hamming space, has attracted intensive attention in recent years. This is motivated by the facts a) the multi-modal data is widespread, e.g., the web images on Flickr are associated with tags, and b) hashing is an effective technique towards large-scale high-dimensional data processing, which is exactly the situation of cross-media retrieval. Inspired by recent advances in deep learning, we propose a cross-media hashing approach based on multi-modal neural networks. By restricting in the learning objective a) the hash codes for relevant cross-media data being similar, and b) the hash codes being discriminative for predicting the class labels, the learned Hamming space is expected to well capture the cross-media semantic relationships and to be semantically discriminative. The experiments on two real-world data sets show that our approach achieves superior cross-media retrieval performance compared with the state-of-the-art methods.

39 citations

Journal ArticleDOI
TL;DR: Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching.

39 citations

Posted Content
TL;DR: In this article, the authors proposed a supervised matrix factorization hashing (SMFH) method for cross-modal visual search, which employs a well-designed binary code learning algorithm to preserve the similarities among multidomain original features through a graph regularization.
Abstract: Matrix factorization has been recently utilized for the task of multi-modal hashing for cross-modality visual search, where basis functions are learned to map data from different modalities to the same Hamming embedding. In this paper, we propose a novel cross-modality hashing algorithm termed Supervised Matrix Factorization Hashing (SMFH) which tackles the multi-modal hashing problem with a collective non-matrix factorization across the different modalities. In particular, SMFH employs a well-designed binary code learning algorithm to preserve the similarities among multi-modal original features through a graph regularization. At the same time, semantic labels, when available, are incorporated into the learning procedure. We conjecture that all these would facilitate to preserve the most relevant information during the binary quantization process, and hence improve the retrieval accuracy. We demonstrate the superior performance of SMFH on three cross-modality visual search benchmarks, i.e., the PASCAL-Sentence, Wiki, and NUS-WIDE, with quantitative comparison to various state-of-the-art methods

38 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202333
202289
202111
202016
201916
201838