Pairwise Linear Regression Classification for Image Set Retrieval
This paper proposes the pairwise linear regression classification (PLRC) for image set retrieval. In PLRC, we first define a new concept of the unrelated subspace and introduce two strategies to constitute the unrelated subspace. In order to increase the information of maximizing the query set and the unrelated image set, we introduce a combination metric for two new classifiers based on two constitution strategies of the unrelated subspace. Extensive experiments on six well-known databases prove that the performance of PLRC is better than that of DLRC and several state-of-the-art classifiers for different vision recognition tasks: cluster-based face recognition, video-based face recognition, object recognition and action recognition.
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Action RecognitionClassificationFace RecognitionGeneral ClassificationObject RecognitionregressionRetrievalTemporal Action LocalizationSimilar Papers 제목 키워드 기반
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