Scene Classification Using Spatial Pyramid of Latent Topics

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 repres...

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Bibliographic Details
Main Authors: Ergul, Emrah, Arica, Nafiz
Format: Conference Proceeding
Language:English
Subjects:
bag of words
Classification algorithms
Feature extraction
Histograms
probabilistic latent semantic analysis
scene classification
Semantics
spatial pyramid matching
Spatial resolution
Support vector machines
Visualization
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Description
Summary: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.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2010.879