1 research outputs found
A unified framework of predicting binary interestingness of images based on discriminant correlation analysis and multiple kernel learning
In the modern content-based image retrieval systems, there is an increasingly
interest in constructing a computationally effective model to predict the
interestingness of images since the measure of image interestingness could
improve the human-centered search satisfaction and the user experience in
different applications. In this paper, we propose a unified framework to
predict the binary interestingness of images based on discriminant correlation
analysis (DCA) and multiple kernel learning (MKL) techniques. More specially,
on the one hand, to reduce feature redundancy in describing the interestingness
cues of images, the DCA or multi-set discriminant correlation analysis (MDCA)
technique is adopted to fuse multiple feature sets of the same type for
individual cues by taking into account the class structure among the samples
involved to describe the three classical interestingness cues,
unusualness,aesthetics as well as general preferences, with three sets of
compact and representative features; on the other hand, to make good use of the
heterogeneity from the three sets of high-level features for describing the
interestingness cues, the SimpleMKL method is employed to enhance the
generalization ability of the built model for the task of the binary
interestingness classification. Experimental results on the publicly-released
interestingness prediction data set have demonstrated the rationality and
effectiveness of the proposed framework in the binary prediction of image
interestingness where we have conducted several groups of comparative studies
across different interestingness feature combinations, different
interestingness cues, as well as different feature types for the three
interestingness cues.Comment: 30 pages, 9 figures, 6 table