Multiple Visual Phrase Learning Method for Image Classification
Li-Rong Dai · Journal of Chinese Computer Systems · 2012
Due to the limited descriptive and discriminative ability of bag of visual words and the problem that traditional learning methods may suffer from background clutters and large appearance variations.We propose a MVPL(Multiple Visual Phrase Learning) method for image classification.In MVPL,the visual phrase is first generated from over-segmented image regions of homogeneous appearance and visual words within each region,which may provide enhanced descriptive ability by introducing the spatial coherency.Then a devised MIL algorithm is applied to efficiently learn from the weakly labeled image data.The experiment results on benchmark dataset Caltech-101[1] and Scene-15[2] show that our proposed method significantly outperforms the state-of-the-art algorithms about 9% and 7% respectively.