The heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: a survey

Abstract

There is a rapid growth of the amount of multimedia data from real-world multimedia sharing web sites, such as Flickr and Youtube. These data are usually of high dimensionality, high order, and large scale. Moreover, different types of media data are interrelated everywhere in a complicated and extensive way by context prior. It is well known that we can obtain lots of features from multimedia such as images and videos; those high-dimensional features often describe various aspects of characteristics in multimedia. However, the obtained features are often over-complete to describe certain semantics. Therefore, the selection of limited discriminative features for certain semantics is hence crucial to make the understanding of multimedia more interpretable. Furthermore, the effective utilization of intrinsic embedding structures in various features can boost the performance of multimedia retrieval. As a result, the appropriate representation of the latent information hidden in the related features is hence crucial during multimedia understanding. This paper introduces many of the recent efforts in sparsity-based heterogenous feature selection, the representation of the intrinsic latent structure embedded in multimedia, and the related hashing index techniques.