Learning attribute relation in attribute-based zero-shot classification
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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The advances in image acquisition techniques make recording images never easier and brings a great convenience to our daily life. It raises at the same time the issue of privacy protection in the photographs. One particular problem addressed in this paper is about covert photographs, which are taken secretly and often violate the subjects' willingness. We study the task of automatic covert photograph classification, which can be used to help inhibiting distribution of such images (e.g., Internet image filtering). By carefully collecting and investigating a large covert vs. non-covert photographs dataset, we observed that there are many features (e.g., degree of blur) that seem to be correlated with covert photographs, but counter examples always exist. In addition, we observed that image visual attributes (e.g., photo composition) play an important role in distinguishing covert photographs. These observations motivate us to fuse both low level images statistics and middle level attribute features for classifying covert images. In particular, we propose a solution using multiple kernel learning to combine 10 different image features and 31 image attributes. We evaluated thoroughly the proposed approach together with many different solutions including some state-of-the-art image classifiers. The effectiveness of the proposed solution is clearly demonstrated in the results. Furthermore, as the first study to this problem, we expect our study to motivate further research investigations.