Design and Analysis of CBIR System using Hybrid PSO and K-Mean Clustering Methods
Pages : 397-401
Images have always been considered an effective medium for presenting visual data in many applications of industry and academia. With the development of technology, a large amount of images are being generated every day. Therefore, managing and indexing of images become essential in order to retrieve similar images effectively. In conventional systems, images are generally indexed with textual annotations. However, as the database grows larger, the use of keywords based methods to retrieve a particular image becomes inefficient. Content-based Image Retrieval (CBIR) systems demonstrate excellent performance at computing low-level features from pixel representations but its output does not reflect the overall desire of the user. In this paper we Proposed Hybrid Approach which is combination of PSO and K-Mean clustering. Therefore the image contents are evaluated using the edge for shape feature extraction; grid color movement analysis is performed for color feature evaluation. Consequently the query image is also extracted for comparison and accurate image extraction. Proposed implementation is provided using MATLAB configuration which is show improved system performance outcomes and accurate image retrieval of the query image.
Keywords: CBIR, PSO, K-Mean Clustering, query image, RGB, histogram
Article published in International Journal of Current Engineering and Technology, Vol.7, No.2 (April-2017)