Discovering Class-Specific Informative Patches and Its Application in Landmark Charaterization

摘要

Discovering class-specific informative regions for a given concept with a few images is an interesting but very challenging task, due to occlusion, scale changes of objects, as well as different views under varying lighting conditions. This paper proposes a new perspective to discover the informative regions by using several images. To achieve this, we introduce a new representation of image: Ordered-BoW Image (BoWI), whose elements summarizes information of the patch centered at the element in original image. Because of its "structured pixels", BoWI is robust and informative enough for an object class representation. Histogram-based Multi-Ranking Amalgamation Strategy (MRAS) is adopted to explore the most informative patches for an object in BoWI. Experiments on Landmark-National Icon data set that our approach is robust to occlusion, scale and illumination, and achieves promising performance in discovering class-specific informative regions.