Concentrated hashing with neighborhood embedding for image retrieval and classification

Dongmei Mo, Wai Keung Wong*, Xianjing Liu, Yao Ge

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Hashing learning is efficient for large-scale image retrieval by using the nearest neighbor search with binary codes instead of continuous representations. With the success of deep neural networks in related tasks such as data representation, recent hashing methods based on deep learning can further improve image retrieval quality and classification accuracy. However, most existing methods are primarily designed to maximize the performance of retrieval based on linear scan of hash codes which is still time-consuming on large-scale datasets. Fortunately, Hamming space retrieval is an alternative as it is less time-consuming by retrieving data points that are within a Hamming ball with a given Hamming radius, but few works focus on that. In this paper, we propose a concentrated hashing method with neighborhood embedding (CHNE) for efficient and effective image retrieval and classification. By integrating Cauchy cross-entropy and pair-wise weighted similarity loss, CHNE can enable similar data pairs with smaller Hamming distance and dissimilar data pairs with larger Hamming distance. In addition, existing hashing methods are usually designed for retrieval, thus the performance of classification using the binary codes is not guaranteed. To tackle this problem, we jointly minimize the regression quantization and neighborhood structure reconstruction errors in the loss function to improve the classification accuracy. The proposed end-to-end deep hashing method can be optimized by back-propagation in a standard manner. Experimental results on several datasets demonstrate that the proposed method can improve the performance of retrieval and classification. Due to its generality, the proposed method is expected to be useful for image retrieval and classification in broader areas.

Original languageEnglish
Pages (from-to)1571-1587
Number of pages17
JournalInternational Journal of Machine Learning and Cybernetics
Volume13
Issue number6
DOIs
Publication statusAccepted/In press - 3 Jan 2022

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