%0 Generic %1 zakariya2014analysis %A Zakariya, S. M. %A Akhtar, Nadeem %D 2014 %I ACEEE (A Computer division of IDES) %K Based Content Features Image Measure Performance Pre-processing Retrieval database steps %T Analysis of Unsupervised Content-based Image Retrieval by Fusing the Image Features %U http://searchdl.org/public/conference/2014/ITC/62.pdf %X In Content-Based Image Retrieval (CBIR) systems, the visual contents of the images in the database are took out and represented by multi-dimensional characteristic vectors. A well known CBIR system that retrieves images by unsupervised method known as cluster based image retrieval system. For enhancing the performance and retrieval rate of CBIR system, we fuse the visual contents of an image. Recently, we developed two cluster-based CBIR systems by fusing the scores of two visual contents of an image. In this paper, we analyzed the performance of the two recommended CBIR systems at different levels of precision using images of varying sizes and resolutions. We also compared the performance of the recommended systems with that of the other two existing CBIR systems namely UFM and CLUE. Experimentally, we find that the recommended systems outperform the other two existing systems and one recommended system also comparatively performed better in every resolution of image.

@conference{zakariya2014analysis, abstract = {In Content-Based Image Retrieval (CBIR) systems, the visual contents of the images in the database are took out and represented by multi-dimensional characteristic vectors. A well known CBIR system that retrieves images by unsupervised method known as cluster based image retrieval system. For enhancing the performance and retrieval rate of CBIR system, we fuse the visual contents of an image. Recently, we developed two cluster-based CBIR systems by fusing the scores of two visual contents of an image. In this paper, we analyzed the performance of the two recommended CBIR systems at different levels of precision using images of varying sizes and resolutions. We also compared the performance of the recommended systems with that of the other two existing CBIR systems namely UFM and CLUE. Experimentally, we find that the recommended systems outperform the other two existing systems and one recommended system also comparatively performed better in every resolution of image. }, added-at = {2014-03-24T05:12:24.000+0100}, author = {Zakariya, S. M. and Akhtar, Nadeem}, biburl = {https://www.bibsonomy.org/bibtex/29d823d475e690631b3683a06d5e2edc6/idescitation}, interhash = {02e4de338546f30a48f41169d31c775a}, intrahash = {9d823d475e690631b3683a06d5e2edc6}, keywords = {Based Content Features Image Measure Performance Pre-processing Retrieval database steps}, organization = {Institute of Doctors Engineers and Scientists}, publisher = {ACEEE (A Computer division of IDES)}, timestamp = {2014-03-24T05:12:24.000+0100}, title = {Analysis of Unsupervised Content-based Image Retrieval by Fusing the Image Features }, url = {http://searchdl.org/public/conference/2014/ITC/62.pdf}, year = 2014 }