Effective Relevance Feedback for Content Based Image Retrieval Using Mining Navigation Pattern and SEO Tools
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 4
Abstract
with a rapid increase of images that are available in social blog and media, image annotation has evolved as an important thing due to its application in image toning and retrieval. Most studies cast image annotation into a multi label classification problem. Images with complete annotations in order to learn a reliable model for tag prediction. We address this limitation by developing a novel approach that combines the strength of tag ranking with the power of matrix recovery. Instead of having to make a decision for each tag, our approach ranks tags in the descending order of their relevance to the given image, significantly simplifying the problem. In addition, the proposed method aggregates the prediction models for different tags into a matrix, and casts tag ranking into a matrix recovery problem, so that a reliable prediction model can be learned for tag ranking even when the tag space is large and the number of training images is limited. Experiments on multiple well-known image data sets demonstrate the effectiveness of the proposed framework for tag ranking compared with the state-of-the-art approaches for image annotation and tag ranking.
Authors and Affiliations
M. Sridharan, N. Bala Sundara Ganapathy, M. Jerome Felix, S. Sivakumar
Keywords
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- EP ID EP21969
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