An efficient framework for image retrieval using color, texture and edge features. (August 2018)

• Record Type: Journal Article
• Authors: Pavithra, L.K.; Sharmila, T. Sree
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 580
• Page End: 593
• Publication Date: 2018-08
• Subjects: Canny edge detection -- Color moments -- Content-Based Image Retrieval (CBIR) -- Local Binary Pattern (LBP) -- Manhattan similarity measure; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.08.030
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: 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.