Modeling Scenes with Local Descriptors and Latent Aspects

We present a new approach to model visual scenes in image collections, based on local invariant features and probabilistic latent space models. Our formulation provides answers to three open questions:(1) whether the invariant local features are suitable for scene (rather than object) classification; (2) whether unsupervised latent space models can be used for feature extraction in the classification task; and (3) whether the latent space formulation can discover visual co-occurrence patterns, motivating novel approaches for image organization and segmentation. Using a 9500-image dataset, our approach is validated on each of these issues. First, we show with extensive experiments on binary and multi-class scene classification tasks, that a bag-of-visterm representation, derived from local invariant descriptors, consistently outperforms state-of-theart approaches. Second, we show that Probabilistic Latent Semantic Analysis (PLSA) generates a compact scene representation, discriminative...

• Type: Conference
• Year: 2005
• Where: ICCV
• Authors: Pedro Quelhas, Florent Monay, Jean-Marc Odobez, Daniel Gatica-Perez, Tinne Tuytelaars, Luc J. Van Gool
• Keywords: Computer Vision, ICCV 2005, Latent Space Formulation, Latent Space Models, Local Invariant Descriptors, Probabilistic Latent Space, Unsupervised Latent Space