Comparing Local Feature Descriptors in pLSA-Based Image Models

Probabilistic models with hidden variables such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have recently become popular for solving several image content analysis tasks. In this work we will use a pLSA model to represent images for performing scene classification. We evaluate the influence of the type of local feature descriptor in this context and compare three different descriptors. Moreover we also examine three different local interest region detectors with respect to their suitability for this task. Our results show that two examined local descriptors, the geometric blur and the self-similarity feature, outperform the commonly used SIFT descriptor