A New Scene Classification Method Based on Spatial Pyramid Matching Model

Abstract

Scene classiflcation is an appealing and challenging problem in image processing and machine vision. Recently, Bag-of-visual-words (BOVW) method using pyramid matching scheme has shown remarkable performance for scene classiflcation. But this method deriving from local keypoints does not contain texture features which are rich in scene images. To further improves the classiflcation accuracy, this paper presents a new method combining Rotation Invariant Local Binary Patterns (RILBP) texture features and BOVW model in spatial pyramid matching framework. First, scene image is subdivided at difierent resolutions for constructing a spatial pyramid. Then based on scale invariant feature transform descriptor and K-means clustering, Pyramid Histogram of visual Words (PHOW) is extracted. And RILBP texture feature is extracted using the mean of a 3*3 neighborhood as threshold. Last we construct a composite kernel of spatial pyramid matching. We regard the keypoint features and texture features as two independent feature channels, and combine them to realize scene classiflcation using one-against-rest SVMs with the composite kernel. Experiments results on the three difierent scene datasets show that our method is efiective.