Probabilistic Model Based Image Retrieval Using Hypothesis Testing
This paper proposes a new approach for content based image retrieval (CBIR). This approach introduces the hypothesis testing into CBIR. The problem of image retrieval is first restated as the process of image verification between the query image and the images in the database after which the candidate images return. This verification process is then translated as a hypothesis testing problem where the query image claims it is a member of the image class where the reprehensive image in the database belongs to. The images accepted by the hypothesis serve as the candidate images and rank according to the value the test ratio takes. The paper intensively studies the problem of constructing background model, image model and scoring by test ratio. The experimental results show satisfying results of the novel approach.
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