Scene categorization using topic model based hierarchical conditional random fields
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Location and route tracking in university from photos without GPS information
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
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We propose a scene classification method, which combines two popular methods in the literature: Spatial Pyramid Matching (SPM) and probabilistic Latent Semantic Analysis (pLSA) modeling. The proposed scheme called Cascaded pLSA performs pLSA in a hierarchical sense after the soft-weighted BoW representation based on dense local features is extracted. We associate spatial layout information by dividing each image into overlapping regions iteratively at different resolution levels and implementing a pLSA model for each region individually. Finally, an image is represented by concatenated topic distributions of each region. In performance evaluation, we compare the proposed method with the most successful methods in the literature, using the popular 15-class-dataset. In the experiments, it is seen that our method slightly outperforms the others in that particular dataset.