A Filtration Strategy Based on FD-CRF for Image Matching

The need for fast retrieving images has recently increased tremendously in many application areas. SIFT-like local descriptor-based matching is widely adopted and has achieved state-of-the-art performance. However, it becomes inefficient when computational and storage resources are limited. Besides, local descriptor-based methods may suffer difficulties when an image pair contains multiple similar local regions. In this work, we propose a novel and effective filtration strategy based on the Conditional Random Field (CRF) model to enhance image retrieval. As the CRF model is used to depict the dependencies of adjacent components, we regard the essential components of an image as the basic structure of CRF. The novel Fourier Descriptor CRF (FD-CRF) method is first proposed to utilize the advantages of CRF and global shape features, then the filtration strategy is adopted to integrate FD-CRF and SIFT-like descriptors for better retrieval results. The experiments demonstrate that our method is practical and outperforms state-of-the-art methods in matching accuracy.