Encoding Visual Attention Features for Effective Biomedical Images Retrieval

This paper proposes a novel model, called Similarity Based on Visual Attention Features (SimVisual), to enhance the similarity analysis between images by considering features extracted from salient regions mapped by visual attention models. Visual attention models have demonstrated to be very useful...

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Bibliographic Details
Main Authors: Pedrosa, Glauco Vitor, Traina, Agma J. M.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Biological system modeling
Biomedical imaging
Color
Computational modeling
Encoding
Feature extraction
Image color analysis
Image enhancement
image processing
Image retrieval
Mathematical models
medical image
Retrieval
Similarity
visual attention features
Visualization
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Summary:This paper proposes a novel model, called Similarity Based on Visual Attention Features (SimVisual), to enhance the similarity analysis between images by considering features extracted from salient regions mapped by visual attention models. Visual attention models have demonstrated to be very useful for encoding perceptual semantic information of the image content. Thus, aggregating saliency features into the final image representation is a powerful asset to enhance the similarity analysis between images, while increasing the accuracy in retrieval tasks. The goal of SimVisual is to combine different saliency models with traditional image descriptors, aimed at increasing the descriptive power of these descriptors without modifying the original algorithms. We performed some experiments using a large dataset composed of 32 different biomedical images categories, and the results show that SimVisual boosts the retrieval accuracy up to 13% considering simple image descriptors, such as Color Histograms. The experiments on SimVisual shows that it is a valuable approach to increase the efficacy of content-based image retrieval systems, without user interactions.
ISSN:2372-9198
DOI:10.1109/CBMS.2016.22