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2015, Volume 16, Issue 10

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Abstract

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Frontiers of Information Technology & Electronic Engineering >> 2015, Volume 16, Issue 10 doi: 10.1631/FITEE.1500070

Beyond bag of latent topics: spatial pyramid matching for scene category recognition

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1. School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China.2. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China

Available online:2015-10-12

Abstract

We propose a heterogeneous, mid-level feature based method for recognizing natural scene categories. The proposed feature introduces spatial information among the latent topics by means of spatial pyramid, while the latent topics are obtained by using probabilistic latent semantic analysis (pLSA) based on the bag-of-words representation. The proposed feature always performs better than standard pLSA because the performance of pLSA is adversely affected in many cases due to the loss of spatial information. By combining various interest point detectors and local region descriptors used in the bag-of-words model, the proposed feature can make further improvement for diverse scene category recognition tasks. We also propose a two-stage framework for multi-class classification. In the first stage, for each of possible detector/descriptor pairs, adaptive boosting classifiers are employed to select the most discriminative topics and further compute posterior probabilities of an unknown image from those selected topics. The second stage uses the prod-max rule to combine information coming from multiple sources and assigns the unknown image to the scene category with the highest ‘final’ posterior probability. Experimental results on three benchmark scene datasets show that the proposed method exceeds most state-of-the-art methods.

Keywords

Scene category recognition ; Probabilistic latent semantic analysis ; Bag-of-words ; Adaptive boosting

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京公网安备 11010502051620号 京ICP备11030251号-2