Histology Image Indexing Using a Non-negative Semantic Embedding
Large on-line collections of biomedical images are becoming more common and may be a potential source of knowledge. An important unsolved issue that is actively investigated is the efficient and effective access to these repositories. A good access strategy demands an appropriate indexing of the collection. This paper presents a new method for indexing histology images using multimodal information, taking advantage of two kinds of data: visual data extracted directly from images and available text data from annotations performed by experts. The new strategy called Non-negative Semantic Embedding defines a mapping between visual an text data assuming that the latent space spanned by text annotations is good enough representation of the images semantic. Evaluation of the proposed method is carried out by comparing it with other strategies, showing a remarkable image search improvement since the proposed approach effectively exploits the image semantic relationships.
KeywordsLatent Factor Visual Feature Text Data Mean Average Precision Semantic Space
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