Simhash for Large Scale Image Retrieval

Qin Zhen Guo; Zhi Zeng; Shu Wu Zhang; Xiao Feng; Hu Guan

doi:10.4028/www.scientific.net/AMM.651-653.2197

Paper Titles

Research and Analysis of Network Data Mining Based on Genetic Algorithm
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Research and Simulation of Data Mining Method Based on the Association Rules Algorithm
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Research on Gridding Algorithm of Remote Sensing Image Segmentation by Region Growth
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Riemann Integrablity of the Composite Function
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Simhash for Large Scale Image Retrieval
p.2197

Study on Threshold Method in Gene Recognition
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Sufficient Conditions of H-Matrix
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The Equivalence Relationship of Matrix and the Corresponding Equivalence Classes
p.2211

The Identity between the Real Number and Order one Matrix
p.2216

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 651-653Simhash for Large Scale Image Retrieval

Simhash for Large Scale Image Retrieval

Abstract:

Due to its fast query speed and reduced storage cost, hashing, which tries to learn binary code representation for data with the expectation of preserving the neighborhood structure in the original data space, has been widely used in a large variety of applications like image retrieval. For most existing image retrieval methods with hashing, there are two main steps: describe images with feature vectors, and then use hashing methods to encode the feature vectors. In this paper, we make two research contributions. First, we creatively propose to use simhash which can be intrinsically combined with the popular image representation method, Bag-of-visual-words (BoW) for image retrieval. Second, we novelly incorporate “locality-sensitive” hashing into simhash to take the correlation of the visual words of BoW into consideration to make similar visual words have similar fingerprint. Extensive experiments have verified the superiority of our method over some state-of-the-art methods for image retrieval task.