Combined feature compression encoding in image retrieval

Abstract

Recently, features extracted by convolutional neural networks (CNNs) are popularly used for image retrieval. In CNN representation, high-level features are usually chosen to represent the images in coarse-grained datasets, while mid-level features are successfully applied to describe the images for fine-grained datasets. In this paper, we combine these different levels of features as a joint feature to propose a robust representation that is suitable for both coarse-grained and fine-grained image retrieval datasets. In addition, in order to solve the problem that the efficiency of image retrieval is influenced by the dimensionality of indexing, a unified subspace learning model named spectral regression (SR) is applied in this paper. We combine SR and the robust representation of the CNN to form a combined feature compression encoding (CFCE) method. CFCE preserve the information without noticeably impacting image retrieval accuracy. We find the tendency of the image retrieval performance to change the compressed dimensionality of features. We further discover a reasonable dimensionality of indexing in image retrieval. Experiments demonstrate that our model provides state-of-the-art performances across datasets.