document: Leveraging auxiliary text terms for automatic image annotation This paper proposes a novel algorithm to annotate web images by automatically aligning the images with their most relevant auxiliary text terms . First , the DOM-based web page segmentation is performed to extract images and their most relevant auxiliary text blocks . Second , automatic image clustering is used to partition the web images into a set of groups according to their visual similarity contexts , which significantly reduces the uncertainty on the relatedness between the images and their auxiliary terms . The semantics of the visually-similar images in the same cluster are then described by the same ranked list of terms which frequently co-occur in their text blocks . Finally , a relevance re-ranking process is performed over a term correlation network to further refine the ranked term list . Our experiments on a large-scale database of web pages have provided very positive results .
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