English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In the modern content-based image retrieval systems, there is an increasinglyinterest in constructing a computationally effective model to predict theinterestingness of images since the measure of image interestingness couldimprove the human-centered search satisfaction and the user experience indifferent applications. In this paper, we propose a unified framework topredict the binary interestingness of images based on discriminant correlationanalysis (DCA) and multiple kernel learning (MKL) techniques. More specially,on the one hand, to reduce feature redundancy in describing the interestingnesscues of images, the DCA or multi-set discriminant correlation analysis (MDCA)technique is adopted to fuse multiple feature sets of the same type forindividual cues by taking into account the class structure among the samplesinvolved to describe the three classical interestingness cues,unusualness,aesthetics as well as general preferences, with three sets ofcompact and representative features; on the other hand, to make good use of theheterogeneity from the three sets of high-level features for describing theinterestingness cues, the SimpleMKL method is employed to enhance thegeneralization ability of the built model for the task of the binaryinterestingness classification. Experimental results on the publicly-releasedinterestingness prediction data set have demonstrated the rationality andeffectiveness of the proposed framework in the binary prediction of imageinterestingness where we have conducted several groups of comparative studiesacross different interestingness feature combinations, differentinterestingness cues, as well as different feature types for the threeinterestingness cues.

A unified framework of predicting binary interestingness of images based on discriminant correlation analysis and multiple kernel learning