An Improved Seed Point Selection-Based Unsupervised Color Clustering for Content-Based Image Retrieval Application. (27th March 2019)

• Record Type: Journal Article
• Authors: Pavithra, L K; Sree Sharmila, T
• Is Part Of: Computer journal. Volume 63:Number 3(2020)
• Page Start: 337
• Page End: 350
• Publication Date: 2019-03-27
• Subjects: dominant color descriptor -- content-based image retrieval -- initial seed point selection -- similarity measure; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxz017
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: The images involved in the content-based image retrieval (CBIR) applications are collectively represented by features such as color, texture and shape. The precision of the CBIR application relies on the key features used in image representation and its similarity measure. In CBIR, dominant color feature extraction is affected by the predefined intervals used in color quantization. The proposed work mainly concentrates on extracting the dominant color information of the image using the clustering process. The clustering process is initiated by the proposed seed point's selection approach. This approach derives the number of seed points using the first order statistical measure and maximum range of the distributed pixel values. Moreover, this work gives equal priority to dominant color and its occurrence information in calculating the similarity between query and database images. Finally, the standard databases such as SIMPLIcity, Corel-10k, OT-scene, Oxford flower and GHIM are taken to investigate the performance of the proposed dominant color based image retrieval application.