An Improved Seed Point Selection- Based Unsupervised Color Clustering for Content-Based Image Retrieval Application

摘要

The images involved in the content-based image retrieval (CBIR) applications are collectivelyrepresented by features such as color, texture and shape. The precision of the CBIR applicationrelies on the key features used in image representation and its similarity measure. In CBIR, dominantcolor feature extraction is affected by the predefined intervals used in color quantization.The proposed work mainly concentrates on extracting the dominant color information of theimage using the clustering process. The clustering process is initiated by the proposed seed point’sselection approach. This approach derives the number of seed points using the first order statisticalmeasure and maximum range of the distributed pixel values. Moreover, this work gives equalpriority to dominant color and its occurrence information in calculating the similarity betweenquery and database images. Finally, the standard databases such as SIMPLIcity, Corel-10k, OTscene,Oxford flower and GHIM are taken to investigate the performance of the proposed dominantcolor based image retrieval application.