Abstract

In this paper we propose a simple but efficient image rep- resentation for solving the scene classification problem. Our new repre- sentation 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 representa- tion model in three datasets which contain a variety of scene categories (15-Scenes, UIUC-Sports and MIT-indoor). Our LPR representation ob- tains 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.