An Efficient Annotation based Image Retrieval System by Mining of Semantically Related user Queries with Improved Markovian Model

Objective: The main objective of this research is to establish the semantic gap between human-understandable high-level semantics and machine generated low-level features for Automatic image annotation in Annotation based Online Image Retrieval system. The semantic gap reduction is also concentrated where there will present more semantic gap between the human and machine defined entities. Methods/Statistical Analysis: Semantic annotated Markovian Semantic Indexing (SMSI) is used for retrieving the images and automatically annotates the images in the database using hidden Markov model. In contrast to traditional annotation based image retrieval system, retrieves images based on low-level features, the proposed SMSI semantically retrieves the images by searching semantically annotated images in a database for a user query. Each image in a large collection of training samples is then annotated automatically with the a posteriori probability of concepts present in it. At last semantic retrieval of images can be done by measuring semantic similarity of annotated images in the large database by using Natural Language processing tool namely WordNet. In addition to that entity based ontology representation is introduced which tend to map the human defined higher level keywords to the machine specific lower lever features. It is achieved by converting the lower level feature values into the intermediate level features. Findings: The presented SMSI method possess definite theoretical advantages and also to achieve better Precision versus Recall results when compared to Latent Semantic Indexing (LSI) and Markovian Semantic Indexing (MSI), methods in Annotation-Based online Image Retrieval system. The better accuracy is achieved while retrieving the contents based image annotation where the semantic gap is reduced considerably. Application/Improvements: Thus the analysis of presented work is demonstrates semantically related features of images and achieves improved retrieval result when compare with the other state-of-art techniques.