Content Based Image Retrieval using Adaptive Semantic Signature

Semantic gap optimization is the major objective of Content Based Image Retrieval(CBIR) system. It is challenging to derive possible query objectives in case of an image having multiple objects or complex background. The query requirements are defined as the Bag of Words(BoW) and used for retrieval of the similar images. The saliency based segmentation is popularly used to eliminate the background objects that works well when the image is focused on the foreground objects. In case saliency-map fail to extract the foreground object, the feature vectors are equally biased by the background scene and that imposter the actual query objectiveThis paper proposes a two phase framework CBIR system. In the first phase we compute the Local Binary Pattern and the RGB Colour information(LBPC) features of all the images in the large image database. Then the pertinent images are clustered and the image on the cluster centre is assumed as ideal indexed image for the same class. In the second phase, each input image is divided into nine blocks having common central sub-block. The LBPC feature is extracted for all the blocks and only the feature of the block that best matches with the indexed image is stored in the database. The selection of the best block image feature minimizes the effect of the background scene. This approach helps select the potential saliency block of the image with respect to the indexed image. The result section shows the performance of the proposed framework while BoW is defined by the cluster indexed image.