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 [BibTeX] [Marc21]
Modeling Scenes with Local Descriptors and Latent Aspects
Type of publication: Idiap-RR
Citation: quelhas:rr04-79
Number: Idiap-RR-79-2004
Year: 2004
Institution: IDIAP
Note: Published in Proc. of IEEE int. Conf. on Computer Vision
Abstract: 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-the-art approaches. Second, we show that Probabilistic Latent Semantic Analysis (PLSA) generates a compact scene representation, discriminative for accurate classification, and significantly more robust when less training data are available. Third, we have exploited the ability of PLSA to automatically extract visually meaningful aspects, to propose new algorithms for aspect-based image ranking and context-sensitive image segmentation.
Userfields: ipdmembership={vision},
Projects Idiap
Authors Quelhas, Pedro
Monay, Florent
Odobez, Jean-Marc
Gatica-Perez, Daniel
Tuytelaars, Tinne
Gool, Luc Van
Crossref by quelhas:iccv:2005
Added by: [UNK]
Total mark: 0
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