Hybrid Approach For Image Search Reranking

Web image retrieval is a challenging task that requires efforts from image processing, link structure analysis, and web text retrieval. In this paper, we propose a re-ranking method to improve web image retrieval by reordering the images retrieved from an image search engine. The re-ranking process is based on a relevance model, which is a probabilistic model that evaluates the relevance of the HTML document linking to the image, and assigns a probability of relevance. The top-ranked images are used as (noisy) training data and an SVM visual classifier is learned to improve the ranking further. We investigate the sensitivity of the cross-validation procedure to this noisy training data. The principal novelty of the overall method is in combining text/metadata and visual features in order to achieve a completely automatic ranking of the images. Human supervision is introduced to learn the model weights offline, prior to the online reranking process The experiment results showed that the re-ranked image retrieval achieved better performance than original web image retrieval, suggesting the effectiveness of the re-ranking method. The relevance model is learned from the Internet without preparing any training data and independent of the underlying algorithm of the image search engines. The re-ranking process should be applicable to any image search engines with little effort