Relevant Image Fetching Using IWD Algorithm with Co-occurrence Matrix and Annotation Features
Abstract
With development of high range bandwidth transfer of data in form of multimedia has also increase. Image retrieval from large database need computation for identifying relevant data as per user query. Hence visual content from the image were fetch and organize to increase the relevancy of work. So selection of image feature with computation steps is an area of research. This paper has utilize co-occurrence matrix content feature from the image with text annotation feature. Here as per class of images cluster were formed by Intelligent water drop genetic algorithm. This algorithm find best set of images for representing clusters and image data were compared with there feature value enhance security of images were also achieved. Real image dataset was utilize for experimental purpose which has five class of image sets. Results shown that proposed model has increase the accuracy of relevancy while fetching time of images were reduced. This reduction of time was possible by using CCM feature.