抽象的

Query Mining for Automatic Annotation and Annotation Based Image Retrieval Using Hidden Markov Model

Shahidha M Meeran, Bineesh V

This paper introduces a method for automatic annotation of images with keywords from a generic vocabulary of concepts or objects for the purpose of annotation based retrieval of images. Automatic annotation of image can be done by using hidden Markov model, whose states represent concepts. The parameters of the model are estimated from a set of training images. Each image in a large test collection is then automatically annotated with the a posteriori probability of concepts present in it. This annotation supports annotation based search of the image-collection via keywords. The relevance of keyword can be constructed using Aggregate Markov Chain (AMC). A stochastic distance between images, based on their annotation and the keyword relevance captured in the AMC is then introduced. Geometric interpretations of the proposed distance are provided u and its relation to a clustering in the keyword space is investigated. We can use WordNet based context vectors for finding similarity between words and also find the similarity between the images. Then the images which has maximum probability to match with the query is retrieved.