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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: A novel unsupervised hashing method, named maximum variance hashing, is proposed, which aims to maximize the total variance of the hash codes while preserving the local structure of the training data and is extended using anchor graphs to reduce the computational cost.
Abstract: With the explosive growth of the data volume in modern applications such as web search and multimedia retrieval, hashing is becoming increasingly important for efficient nearest neighbor (similar item) search. Recently, a number of data-dependent methods have been developed, reflecting the great potential of learning for hashing. Inspired by the classic nonlinear dimensionality reduction algorithm—maximum variance unfolding, we propose a novel unsupervised hashing method, named maximum variance hashing, in this work. The idea is to maximize the total variance of the hash codes while preserving the local structure of the training data. To solve the derived optimization problem, we propose a column generation algorithm, which directly learns the binary-valued hash functions. We then extend it using anchor graphs to reduce the computational cost. Experiments on large-scale image datasets demonstrate that the proposed method outperforms state-of-the-art hashing methods in many cases.

2 citations

Journal ArticleDOI
TL;DR: A key-dependent code called random Gray (RGray) code for image hashing is proposed, which is likely to increase the security of image hashing to some extent and meanwhile maintains the performance of Gray code in terms of the tradeoff between robustness and fragility.

2 citations

Journal ArticleDOI
TL;DR: This work develops an effective learning-based hashing model, namely local feature hashing with binary auto-encoder (LFH-BAE), to directly learn local binary descriptors in the Hamming space to well reconstruct the face image from binary codes.
Abstract: The learning-based hashing has recently made encouraging progress in face recognition. However, most existing hashing methods disregard the discrete constraint during optimization, inducing the accumulated quantization errors. In this work, we develop an effective learning-based hashing model, namely local feature hashing with binary auto-encoder (LFH-BAE), to directly learn local binary descriptors in the Hamming space. It attempts to exploit structure factors to well reconstruct the face image from binary codes. Specifically, we first introduce a binary auto-encoder to learn a hashing function to project each face region into high-quality binary codes. Since the original problem is a tricky combinational function, we then present a softened version to decompose it into separate tractable sub-problems. Next, we propose an effective alternating algorithm based on the augmented Lagrange method (ALM) to solve these sub-problems, which helps to generate strong discriminative and excellent robust binary codes. Moreover, we utilize the discrete cyclic coordinate descent (DCC) method to optimize binary codes to reduce the loss of useful information. Lastly, we cluster and pool the obtained binary codes, and construct a histogram feature as the final face representation for each image. Extensive experimental results on four public datasets including FERET, CAS-PEAL-R1, LFW and PaSC show that our LFH-BAE is superior to most state-of-the-art face recognition algorithms.

2 citations

Posted Content
TL;DR: The results show that feature hashing can be applied to obtain word embeddings in linear time with the size of the data and that this algorithm, that does not need training, is able to capture the semantic meaning of words.
Abstract: In this paper we propose the application of feature hashing to create word embeddings for natural language processing. Feature hashing has been used successfully to create document vectors in related tasks like document classification. In this work we show that feature hashing can be applied to obtain word embeddings in linear time with the size of the data. The results show that this algorithm, that does not need training, is able to capture the semantic meaning of words. We compare the results against GloVe showing that they are similar. As far as we know this is the first application of feature hashing to the word embeddings problem and the results indicate this is a scalable technique with practical results for NLP applications.

2 citations

Posted Content
TL;DR: In this article, the authors evaluate performance of data-dependent hashing methods on binary data and find a hashing method that can effectively produce lower dimensional binary representation of 512-bit FREAK descriptors.
Abstract: In this paper we evaluate performance of data-dependent hashing methods on binary data. The goal is to find a hashing method that can effectively produce lower dimensional binary representation of 512-bit FREAK descriptors. A representative sample of recent unsupervised, semi-supervised and supervised hashing methods was experimentally evaluated on large datasets of labelled binary FREAK feature descriptors.

2 citations


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