Image retrieval with hierarchical matching pursuit
This addresses the issue of fixed-scale feature extraction degrading performance in image retrieval systems, offering a domain-specific improvement.
The paper tackles the problem of image retrieval by introducing a hierarchical sparse coding architecture that fuses multi-scale cues, achieving excellent retrieval performance on the Holidays dataset with a small code length.
A novel representation of images for image retrieval is introduced in this paper, by using a new type of feature with remarkable discriminative power. Despite the multi-scale nature of objects, most existing models perform feature extraction on a fixed scale, which will inevitably degrade the performance of the whole system. Motivated by this, we introduce a hierarchical sparse coding architecture for image retrieval to explore multi-scale cues. Sparse codes extracted on lower layers are transmitted to higher layers recursively. With this mechanism, cues from different scales are fused. Experiments on the Holidays dataset show that the proposed method achieves an excellent retrieval performance with a small code length.