A Modified Local Binary Pattern (LBP) for Content-based Image Retrieval

Abstract

       Researchers consider Content-Based Image Retrieval (CBIR) as one of the challenging ground as the searching of an image does not depend on manually assigned annotations. Instead, it uses discriminative features to retrieve a query image. Local Binary Pattern (LBP) technique is extensively used in literature to extract texture features; however, it takes more execution time as it considered all the 8 bits while calculating LBP values. In this paper, we proposed a modified version of the LBP (m-LBP) technique that uses only the 4 most significant bits (MSB). The robustness of the proposed method is investigated on two publicly available datasets, namely WANG and SIMPSON images. The similarity score is measured using Euclidean and Manhattan distance-based metrics. Experimental results reveal the effectiveness of the proposed algorithm in terms of precision and recall values and execution time in comparison to the existing state of the art techniques.