Pooling region learning of visual word for image classification using bag-of-visual-words model.

ABSTRACT: In the problem where there is not enough data to use Deep Learning, Bag-of-Visual-Words (BoVW) is still a good alternative for image classification. In BoVW model, many pooling methods are proposed to incorporate the spatial information of local feature into the image representation vector, but none of the methods devote to making each visual word have its own pooling regions. The practice of designing the same pooling regions for all the words restrains the discriminability of image representation, since the spatial distributions of the local features indexed by different visual words are not same. In this paper, we propose to make each visual word have its own pooling regions, and raise a simple yet effective method for learning pooling region. Concretely, a kind of small window named ob

SUBMITTER: Xu Y 

PROVIDER: S-EPMC7274423 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature