Exploiting Deep Contrast Feature for Image Retrieval.

Background: In the field of content-based image retrieval (CBIR), fused feature-based methods have demonstrated their advanced performance on the popular benchmark datasets. However, it is inevitable increase the vector dimensionality because the fused features have diversity. Therefore, achieving both a low-dimensional representation and high retrieval performance remains challenging. Methods: To address this problem, an image retrieval method based on the deep contrast-based layer is proposed, namely the deep contrast feature histogram (DCFH), to image retrieval. There are three highlights as follows: (1) texture features based on the edge orientation are calculated to build contrast-based layer; it can enhance the discriminative power of deep features; (2) a generalized mean aggregation method is introduced to effectively aggregate the representative information in the deep feature maps of convolutional neural network (CNN); (3) a multi-orientational PCA whitening method is proposed to provide a compact yet discriminative representation. Results: Comparative experiments demonstrated that our method can provide outstandingly competitive retrieval performance on popular benchmark datasets. Conclusions: This work captures visual information from both global and local perspectives, presenting an approach in line with human visual cognitive. Experiments demonstrated that our method can efficiently combine the strengths of various features to provide the robust representation, thereby improving the retrieval performance. Moreover, our method is easily to be implemented without requiring to retrain the CNN models and not the use of additional supervision. [ABSTRACT FROM AUTHOR]