Enhanced Markovian Model for Image Retrieval Using Synonymous Keywords

Image indexing and retrieval has been an active research area for more than a decade. Even if many achievements have been made in this region, it is a challenging issue and mile from being solved. Classical content-based approaches make use of queries based on image examples or image attributes like color and structure, and images are retrieved according to the similarity of each target image with the query image. Despite, image query based retrieval systems do not really capture the semantics or meanings of images well. Furthermore, image queries are difficult and inconvenient to form for most users. Annotation based image retrieval is one of the promising image retrieval technique because of its power to represent user queries and semantic substance of images. There are many way presented for annotation-based retrieval as well as automatic annotation indexing of images. Currently Markov chain based process, Markovian Semantic Indexing is popularized which is efficient as compared to all previous methods. In this the user propose a unique methodology for classification and annotation-based retrieval of pictures. The new technique is also applicative in the context of an online image retrieval system. The new process is shown to possess bound theoretical blessings and additionally to attain higher precision versus Recall results compared to existing technique of Markovian Semantic Indexing in Annotation Based Image Retrieval. The proposed system extends Markovian Semantic Indexing by considering multiple keywords and synonyms in the user query. The inventive results show the performance of the proposed system.