New non-negative sparse feature learning approach for content-based image retrieval

摘要

One key issue in content-based image retrieval is to extract effective features so as to represent the visual content of an image. In this study, a new non-negative sparse feature learning approach to produce a holistic image representation based on low-level local features is presented. Specifically, a modified spectral clustering method is introduced to learn a non-negative visual dictionary from local features of training images. A non-negative sparse feature encoding method termed non-negative locality-constrained linear coding (NNLLC) is proposed to improve the popular locality-constrained linear coding method so as to obtain more meaningful and interpretable sparse codes for feature representation. Moreover, a new feature pooling strategy named kMaxSum pooling is proposed to alleviate the information loss of the sum pooling or max pooling strategy, which produces a more effective holistic image representation and can be viewed as a generalisation of the sum and max pooling strategies. The retrieval results carried out on two public image databases demonstrate the effectiveness of the proposed approach.