Bayesian Classification for Image Retrieval Using Visual Dictionary

Abstract

Image Retrieval is one of the most promising technologies for retrieving images through the query image. It enables the user to search for the images based upon the relevance of the query image. The main objective of this paper is to develop a faster and more accurate image retrieval system for a dynamic environment such as World Wide Web (WWW). The image retrieval is done by considering color, texture, and edge features. The bag-of-words model can be applied to image classification, by treating image features as words. The goal is to improve the retrieval speed and accuracy of the image retrieval systems which can be achieved through extracting visual features. The global color space model and dense SIFT feature extraction technique have been used to generate a visual dictionary using Bayesian algorithm. The images are transformed into set of features. These features are used as an input in Bayesian algorithm for generating the code word to form a visual dictionary. These code words are used to represent images semantically to form visual labels using Bag-of-Features (BoF). Then it can be extended by combining more features and their combinations. The color and bitmap method involves extracting only the local and global features such as mean and standard deviation. But in this classification technique, color, texture, and edge features are extracted and then Bayesian Algorithm is applied on these image features which gives acceptable classification in order to increases the accuracy of image retrieval.