Regularizing translation models for better automatic image annotation

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Abstract
The goal of automatic image annotation is to automatically generate annotations for images to describe their content. In the past, statistical machine translation models have been successfully applied to automatic image annotation task [8]. It views the process of annotating images as a process of translating the content from a 'visual language' to textual words. One problem with the existing translation models is that common words are usually associated with too many different image regions. As a result, uncommon words have little chance to be used for annotating images. Uncommon words are important for automatic image annotation because they are often used in the queries. In this paper, we propose two modified translation models for automatic image annotation, namely the normalized translation model and the regularized translation model, that specifically address the problem of common annotated words. The basic idea is to raise the number of blobs that are associated with uncommon words. The normalized translation model realizes this by scaling translation probabilities of different words with different factors. The same goal is achieved in the regularized translation model through the introduction of a special Dirichlet prior. Empirical study with the Corel dataset has shown that both two modified translation models outperform the original translation model and several existing approaches for automatic image annotation substantially.

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