LOCAL BINARY PATTERN WITH SUPPORT VECTOR MACHINE TO ENHANCE IMAGE RETRIEVAL

Abstract

Image search techniques were not generally based on visual features but on the textual annotation of images. Images were firstly annotated with text and then searched using a text-based approach from traditional database management systems which is time consuming and difficult to manage. To overcome this problem, content based image retrieval is introduced which is becoming the hottest research area these days due to vast range of real time applications such as crime prevention, photograph archives, medical diagnosis, geographical information and remote sensing system etc. The CBIR system consist of various phases to extract and match the features and search the images from the large scale image databases on the basis of visual contents such as color, shape and texture according to the user's interest. During retrieval, features and descriptors of the query image are compared to those of the images in the database in order to rank each image according to its distance to the query. In our research work, a hybrid combination of SVM (support vector machine) and LBP (Local Binary Pattern) is applied to retrieve the images from the database. A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyper plane. Local Binary Pattern (LBP) is one of the techniques used in image classification based on texture. This operator is simple and very effective, which labels the image pixels based on their neighboring and consider the result being a binary number.