01727nas a2200217   4500000000100000000000100001008004100002260001200043653001100055653002300066653001500089653002300104100001900127700002000146245006900166856009800235300001000333490000600343520114600349022001401495       2016                            d  c12/201610aKmeans10aGenetic Algorithms10aClustering10aFeature Extraction1 aRashmi Welekar1 aPreeti Kushwaha00aFeature Selection for Image Retrieval based on Genetic Algorithm  uhttp://www.ijimai.org/journal/sites/default/files/files/2016/11/ijimai20164_2_3_pdf_46881.pdf  a16-210 v43 aThis paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction such as colour, texture and shape. The three techniques are used for feature extraction such as colour moment, gray level co- occurrence matrix and edge histogram descriptor. To reduce curse of dimensionality and find best optimal features from feature set using feature selection based on genetic algorithm. These features are divided into similar image classes using clustering for fast retrieval and improve the execution time. Clustering technique is done by k-means algorithm. The experimental result shows feature selection using GA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to normal image retrieval system. The result also shows precision and recall of proposed approach compared to previous approach for each image class. The CBIR system is more efficient and better performs using feature selection based on Genetic Algorithm.  a1989-1660