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Latent Pyramidal Regions for Recognizing Scenes
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BibTeX
@MISC{Sadeghi_latentpyramidal,
author = {Fereshteh Sadeghi},
title = {Latent Pyramidal Regions for Recognizing Scenes},
year = {}
}
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Abstract
Abstract. In this paper we propose a simple but efficient image representation for solving the scene classification problem. Our new representation combines the benefits of spatial pyramid representation using nonlinear feature coding and latent Support Vector Machine (LSVM) to train a set of Latent Pyramidal Regions (LPR). Each of our LPRs captures a discriminative characteristic of the scenes and is trained by searching over all possible sub-windows of the images in a latent SVM training procedure. Each LPR is represented in a spatial pyramid and uses non-linear locality constraint coding for learning both shape and texture patterns of the scene. The final response of the LPRs form a single feature vector which we call the LPR representation and can be used for the classification task. We tested our proposed scene representation model in three datasets which contain a variety of scene categories (15-Scenes, UIUC-Sports and MIT-indoor). Our LPR representation obtains state-of-the-art results on all these datasets which shows that it can simultaneously model the global and local scene characteristics in a single framework and is general enough to be used for both indoor and outdoor scene classification. 1
Keyphrases
latent pyramidal region recognizing scene lpr representation latent svm training procedure single feature vector efficient image representation scene category texture pattern scene representation model spatial pyramid representation nonlinear feature coding local scene characteristic lprs form final response spatial pyramid classification task non-linear locality constraint scene classification problem new representation latent support vector machine state-of-the-art result outdoor scene classification discriminative characteristic single framework possible sub-windows
