Randomized Spatial Partition for Scene Recognition

Yuning Jiang, Junsong Yuan, Gang Yu

Abstract: The spatial layout of images plays a critical role in natural scene analysis. Despite previous work, e.g., spatial pyramid matching, how to design optimal spatial layout for scene classification remains an open problem due to the large variations of scene categories. This paper presents a novel image representation method, with the objective to characterize the image layout by various patterns, in the form of randomized spatial partition (RSP). The RSP-based image representation makes it possible to mine the most descriptive image layout pattern for each category of scenes, and then combine them by training a discriminative classifier, i.e., the proposed ORSP classifier. Besides RSP image representation, another powerful classifier, called the BRSP classifier, is also proposed. By weighting and boosting a sequence of various partition patterns, the BRSP classifier is more robust to the intra-class variations hence leads to a more accurate classification. Both RSP-based classifiers are tested on three publicly available scene datasets. The experimental results highlight the effectiveness of the proposed methods.