Research And Implementation On Image Annotation Using Probability Modeling

Automatic image annotation is a challenging work to solve the problem of manually annotation; it tries to build a bridge between the semantic features in high-level and bottom visual features. Especially with the development of machine learning theory, many researchers have designed different learning models about automatic annotation algorithms, which generally can be divided into two categories: probability-based model and classifier-based model.This paper studies two representative annotation algorithms using probability models firstly. They are the Co-occurrence Model and the Translation Model. In the first model they observe the co-occurrence of keywords with image regions which are created using a regular grid. And they annotate the images by the association probability. The Translation Model is a substantial improvement on the Co-occurrence Model. It provides a new concept to describe images using a vocabulary of blobs. Blobs are generated from image features using clustering. Each image is generated by using a certain number of its blobs. They assume that image annotation can be viewed as the process of translating from a vocabulary of blobs to a vocabulary of keywords. Based on Co-occurrence Model and Translation Model, the paper improves and uses a relevance model. For a training set of images, each image in the set has a dual representation in terms of both keywords and blobs. Given a test image, we adopt a generative language modeling approach and assume that there exists some underlying probability distribution, referred to relevance model. The model can be thought of as a set that contains all possible blobs that could appear in the image, as well as all words. So the annotation process is the result of random samples from it. It is to develop probabilistic models to estimate the conditional probability between words and blobs by the training set. This model gets a better significantly performance on the large set of annotated images. Experiments on Corel image databases show the effectiveness and efficiency of the proposed approach.