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An efficient framework for image retrieval using color, texture and edge features
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DOI：10.1016/j.compeleceng.2017.08.030.png)
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This paper proposes a new hybrid framework for Content-Based Image Retrieval (CBIR) system to address the accuracy issues associated with the traditional image retrieval systems. The proposed framework initially selects pertinent images from a large database using color moment information. Subsequently, Local Binary Pattern (LBP) and Canny edge detection methods are used to extract the texture and edge features respectively, from the query and resultant images of the initial stage of this framework. Then, the Manhattan distance information about these two features corresponding to the query and selected images are calculated and combined, and then sorted using bubble sort algorithm. Wang's, Corel-5K and Corel-10K are the three databases used for evaluating the performance of the proposed hybrid framework using precision and recall measures. The average precision measured on these three databases gives approximately 11.8%-22.315%, 8.025%-18.935% and 10.755%-32.221% higher accuracy than the state-of-the-art techniques. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword:
Canny edge detection
Color moments
Content-Based Image Retrieval (CBIR)
Local Binary Pattern (LBP)
Manhattan similarity measure
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