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Entropy 2014, 16(7), 3848-3865;

Hierarchical Geometry Verification via Maximum Entropy Saliency in Image Retrieval

School of Computer Science and Technology, Jilin University, Changchun 130012, China
Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China
Author to whom correspondence should be addressed.
Received: 5 April 2014 / Revised: 16 June 2014 / Accepted: 30 June 2014 / Published: 14 July 2014
(This article belongs to the Special Issue Maximum Entropy and Its Application)
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We propose a new geometric verification method in image retrieval—Hierarchical Geometry Verification via Maximum Entropy Saliency (HGV)—which aims at filtering the redundant matches and remaining the information of retrieval target in images which is partly out of the salient regions with hierarchical saliency and also fully exploring the geometric context of all visual words in images. First of all, we obtain hierarchical salient regions of a query image based on the maximum entropy principle and label visual features with salient tags. The tags added to the feature descriptors are used to compute the saliency matching score, and the scores are regarded as the weight information in the geometry verification step. Second we define a spatial pattern as a triangle composed of three matched features and evaluate the similarity between every two spatial patterns. Finally, we sum all spatial matching scores with weights to generate the final ranking list. Experiment results prove that Hierarchical Geometry Verification based on Maximum Entropy Saliency can not only improve retrieval accuracy, but also reduce the time consumption of the full retrieval. View Full-Text
Keywords: image retrieval; geometry verification; saliency detection; maximum entropy image retrieval; geometry verification; saliency detection; maximum entropy
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Zhao, H.; Li, Q.; Liu, P. Hierarchical Geometry Verification via Maximum Entropy Saliency in Image Retrieval. Entropy 2014, 16, 3848-3865.

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