Bag of Features with Co-occurrence Fusing Visual Words Mutual Information Based on Random Walk Model
A classic scheme to retrieve image is to extract local interesting points and use bag of features to represent an image. In traditional bag of features method, every local interesting point’s assignment is mutually independent, the scheme does not consider the interaction between two adjacent local interesting points’ assignment. So this can incur unreliable assignment for some local interesting points, and low the overall accuracy of image retrieve. Considers that learn some valuable priori knowledge by counting the co-occurring times between visual words; then fuses the co-occurrence information between visual words into the original soft-assigned bag of features based on random walk model, in order to avoid unreliable assignment of local interesting points and improve overall accuracy of image retrieve.
Local Interesting PointVisual WordBag of FeaturesMutual InformationRandom Walk Model
NETL
NSTL
万方数据
维普
