A Thousand Words in a Scene

— This paper presents a novel approach for visual scene modeling and classification, investigating the combined use of text modeling methods and local invariant features. Our work attempts to elucidate (1) whether a text-like bag-of-visterms representation (histogram of quantized local visual features) is suitable for scene (rather than object) classification, (2) whether some analogies between discrete scene representations and text documents exist, and (3) whether unsupervised, latent space models can be used both as feature extractors for the classification task and to discover patterns of visual co-occurrence. Using several data sets, we validate our approach, presenting and discussing experiments on each of these issues. We first show, with extensive experiments on binary and multi-class scene classification tasks using a 9500-image data set, that the bag-of-visterms representation consistently outperforms classical scene classification approaches. In other data sets we sh...

• Type: Journal
• Year: 2007
• Where: PAMI
• Authors: Pedro Quelhas, Florent Monay, Jean-Marc Odobez, Daniel Gatica-Perez, Tinne Tuytelaars
• Keywords: Discrete Scene Representations, PAMI 2007, Scene Classification, Scene Representation