Visual saliency guided complex image retrieval

Haoxiang Wang, Zhihui Li, Yang Li, B. B. Gupta, Chang Choi

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)

Abstract

Compared with the traditional text data, multimedia data are concise and contains rich meanings, so people are more willing to use the multimedia data to store information. How to effectively retrieve information is essential. This paper proposes a novel visual saliency guided complex image retrieval model. Initially, Itti visual saliency model is presented. In this model, the overall saliency map is generated by the integration of direction, intensity and color saliency map, respectively. Then, to help describe the image pattern more clearly, we present the multi-feature fusion paradigm of images. To address the complexity of the images, we propose a two-stage definition: (1) Cognitive load based complexity; (2) Cognitive level of complexity classification. The group sparse logistic regression model is integrated to finalize the image retrieval system. The performance of the proposed system is tested on different databases compared with the other state-of-the-art models which overcome the baselines in complex scenarios.

Original languageEnglish
Pages (from-to)64-72
Number of pages9
JournalPattern Recognition Letters
Volume130
DOIs
Publication statusPublished - Feb 2020
Externally publishedYes

Keywords

  • Complex image
  • Feature extraction
  • Image retrieval
  • Visual saliency

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