Hadoop based CBIR using the Integration of Color and MDLEP
P. Rohini, L. Koteswara Rao · International journal of advance research and innovative ideas in education · 2017
In the process of image retrieval, more information can be extracted by combining two or more features. Feature vectors based on local patterns are very popular in deriving the local information present in an image. Majority of these methods are mainly based on encoding the variation in gray scale values of centre pixel and its neighboring elements. The centre pixel is assigned a value that gets reflected in a Histogram. LBP operator became the first of its kind where the intensity value of centre pixel is treated as threshold to capture the information by comparing with other neighbors. However, the information directions are not explored in the method The DLEPs are proposed to code the edge information mainly in four directions. The performance of Directional local extrema patterns can be improved by taking the magnitude into consideration. In this paper, we propose a new method to improve the performance of the retrieval system with the help of Hadoop framework. The main objective is distribution of image data over a large number of nodes over Hadoop using Map Reduce Technique. Hadoop defines a framework which allows processing on distributed large sets across clusters of computer.