Open Access

ARTICLE

Multi-Index Image Retrieval Hash Algorithm Based on Multi-View Feature Coding

by Rong Duan, Junshan Tan, Jiaohua Qin, Xuyu Xiang, Yun Tan, Neal N. Xiong

1 College of Computer Science and Information Technology, Central South University of Forestry & Technology, Changsha, 410004, China.
2 Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK 74464, USA.

* Corresponding Author: Junshan Tan. Email: .

Computers, Materials & Continua 2020, 65(3), 2335-2350. https://doi.org/10.32604/cmc.2020.012161

Received 17 June 2020; Accepted 26 July 2020; Issue published 16 September 2020

Abstract

In recent years, with the massive growth of image data, how to match the image required by users quickly and efficiently becomes a challenge. Compared with single-view feature, multi-view feature is more accurate to describe image information. The advantages of hash method in reducing data storage and improving efficiency also make us study how to effectively apply to large-scale image retrieval. In this paper, a hash algorithm of multi-index image retrieval based on multi-view feature coding is proposed. By learning the data correlation between different views, this algorithm uses multi-view data with deeper level image semantics to achieve better retrieval results. This algorithm uses a quantitative hash method to generate binary sequences, and uses the hash code generated by the association features to construct database inverted index files, so as to reduce the memory burden and promote the efficient matching. In order to reduce the matching error of hash code and ensure the retrieval accuracy, this algorithm uses inverted multi-index structure instead of single-index structure. Compared with other advanced image retrieval method, this method has better retrieval performance.

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