Content Based Image Retrieval System Using Feature Classification with Modified KNN Algorithm
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - July Issue 2013 by IJCTT Journal|
|Volume-4 Issue-7 |
|Year of Publication : 2013|
|Authors :T. Dharani, I. Laurence Aroquiaraj|
T. Dharani, I. Laurence Aroquiaraj"Content Based Image Retrieval System Using Feature Classification with Modified KNN Algorithm"International Journal of Computer Trends and Technology (IJCTT),V4(7):2008-2013 July Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: - — Feature means countenance, remote sensing scene objects with similar characteristics, associated to interesting scene elements in the image formation process. They are classified into three types in image processing, that is low, middle and high. Low level features are color, texture and middle level feature is shape and high level feature is semantic gap of objects. An image retrieval system is a computer system for browsing, searching and retrieving images from a large image database. Content Based Image Retrieval (CBIR) is a technique which uses visual features of image such as color, shape, texture, etc…to search user required image from large image database according to user’s requests in the form of a query. MKNN is an enhancing method of KNN. The proposed KNN classification is called MKNN. MKNN contains two parts for processing, they are validity of the train samples and applying weighted KNN. The validity of each point is computed according to its neighbors. In our proposal, Modified K-Nearest Neighbor (MKNN) can be considered a kind of weighted KNN so that the query label is approximated by weighting the neighbors of the query. The procedure computes the fraction of the same labeled neighbors to the total number of neighbors. MKNN classification is based on validated neighbors who have more information in comparison with simple class labels. This paper also concentrates identifying the unlabeled images with help of MKNN algorithm. Experiments show the validity takes into accounts the value of stability and robustness of the any train samples regarding with its neighbors and excellent improvement in the performance of KNN method. This system allows provide label to unlabeled image as user input.
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Keywords : — CBIR, Image Classification, KNN, MKNN, Unlabeled image.