Table of Content

Open Access iconOpen Access

ARTICLE

crossmark

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

Rong Duan1, Junshan Tan1, *, Jiaohua Qin1, Xuyu Xiang1, Yun Tan1, Neal N. Xiong2

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: email.

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

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.

Keywords


Cite This Article

R. Duan, J. Tan, J. Qin, X. Xiang, Y. Tan et al., "Multi-index image retrieval hash algorithm based on multi-view feature coding," Computers, Materials & Continua, vol. 65, no.3, pp. 2335–2350, 2020. https://doi.org/10.32604/cmc.2020.012161



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1725

    View

  • 1118

    Download

  • 0

    Like

Share Link