Image Classification by Selective
Regularized Sub spaceLearning
Abstract— Image Classification by Selective Regularized Sub spaceLearning. Feature learning is an intensively studied research topic in image classiﬁcation. Although existing methods like sparse coding, locality-constrained linear coding, ﬁsher vector encoding, etc., have shown their effectiveness in image representation, most of them overlook a phenomenon called the small sample size problem, where the number of training samples is relatively smaller than the dimensionality of the features, which may limit the predictive power of the classiﬁer. Subspace learning is a strategy to mitigate this problem by reducing the dimensionality of the features. However, most conventional subspace learning methods attempt to learn a global subspace to discriminate all the classes, which proves to be < Final Year Projects 2016 > difﬁcult and ineffective in multi-class classiﬁcation task. To this end, we propose to learn a local subspace for each sample instead of learning a global subspace for all samples. Our key observation is that, in multi-class image classiﬁcation, the label of each testing sample is only confused by a few classes which have very similar visual appearance to it. Thus, in this work, we propose a coarse-to-ﬁne strategy, which ﬁrst picks out such classes, and then conducts a local subspace learning to discriminate them. As the subspace learning method is regularized and conducted within some selected classes, we term it se lective regularized subspace learning < SRSL >, and we term our classiﬁcation pipeline selective regularized subspace learning based multi-class image classiﬁcation (SRSL_MIC). Experimental
results on four representative datasets (Caltech-101, Indoor-67, ORL Faces and AR Faces) demonstrate the effectiveness of the proposed method.
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