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Multichannel Local Ternary Co-occurrence Pattern for Content-Based Image Retrieval

  • Megha AgarwalEmail author
  • R. P. Maheshwari
Research Paper

Abstract

In this paper, a novel content descriptor, multichannel local ternary co-occurrence pattern (MCLTCoP) is proposed for image retrieval. Standard local ternary co-occurrence pattern extracts the relationship between center pixel and surrounding pixels by computing gray-level differences in terms of ternary edges. In the modern scenario due to proliferation of color images, it is not sufficient to investigate image appearance variations by analyzing only one type of feature such as color or texture. In order to address this problem, proposed texture patterns are developed to unfold inter-channel variability of R, G and B channels with oppugnant color channel V of the HSV color space. Texture statistics extracted in such a manner not only explore two different color channels but mutual local directional information is also incorporated. This paves a way for improvement in retrieval results. Image retrieval experiments are performed to observe the effectiveness of the proposed feature and compare results with the existing multichannel techniques over benchmark image database, i.e., Corel 1k. Retrieval results show superiority of the proposed approach against state-of-the-art techniques in terms of average precision and average recall.

Keywords

Content-based image retrieval (CBIR) Local binary pattern (LBP) Corel database 

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Copyright information

© Shiraz University 2019

Authors and Affiliations

  1. 1.Jaypee Institute of Information TechnologyNoidaIndia
  2. 2.Indian Institute of Technology RoorkeeRoorkeeIndia

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