This paper deals with coding of natural scenes in order to extract semantic information. We present a new scheme to project natural scenes onto a basis in which each dimension encodes statistically independent information. Basis extraction is performed by independent component analysis (ICA) applied to image patches culled from natural scenes. The study of the resulting coding units (coding filters) extracted from well-chosen categories of images shows that they adapt and respond selectively to discriminant features in natural scenes. Given this basis, we define global and local image signatures relying on the maximal activity of filters on the input image. Locally, the construction of the signature takes into account the spatial distribution of the maximal responses within the image. We propose a criterion to reduce the size of the space of representation for faster computation. The proposed approach is tested in the context of texture classification (111 classes), as well as natural scenes classification (11 categories, 2037 images). Using a common protocol, the other commonly used descriptors have at most 47.7% accuracy on average while our method obtains performances of up to 63.8%. We show that this advantage does not depend on the size of the signature and demonstrate the efficiency of the proposed criterion to select ICA filters and reduce the dimension.

Natural image retrieval is a main challenge for image indexing. In this domain, colour information is one of the most important features. We propose a perceptive colour space based on a biological model of the retina. In this space, a model of Gaussian mixture appears very efficient for colour distribution. Two strategies are presented, retrieval by maximum likelihood (i) on the global chromatic distribution, (ii) on the local spatio-chromatic distribution.

Audiovisual speech recognition involves fusion of the audio and video sensors for phonetic identification. There are three basic ways to fuse data streams for taking a decision such as phoneme identification: data-to-decision, decision-to-decision, and data-to-data. This leads to four possible models for audiovisual speech recognition, that is direct identification in the first case, separate identification in the second one, and two variants of the third early integration case, namely dominant recoding or motor recoding. However, no systematic comparison of these models is available in the literature. We propose an implementation of these four models, and submit them to a benchmark test. For this aim, we use a noisy-vowel corpus tested on two recognition paradigms in which the systems are tested at noise levels higher than those used for learning. In one of these paradigms, the signal-to-noise ratio (SNR) value is provided to the recognition systems, in the other it is not. We also introduce a new criterion for evaluating performances, based on transmitted information on individual phonetic features. In light of the compared performances of the four models with the two recognition paradigms, we discuss the advantages and drawbacks of these models, leading to proposals for data representation, fusion architecture, and control of the fusion process through sensor reliability.

The paper involves the recognition of French audiovisual vowels at various signal-to-noise ratios (SNRs). It deals with a new non-linear preprocessing of the audio data which enables an estimation of the reliability of the audio sensor in relation to SNR, and a significant increase in the recognition performances at the output of the fusion process.

This paper presents a comparison of four basic architectures dealing with audiovisual speech in a noisy-vowel recognition task. Provided contextual input (signal-to-noise ratio), three of the four architectures respect the "synergy" criterion which means that audiovisual (AV) recognition is better than audio-alone (A) or visual-alone (V) recognition, both in global terms and for each individual phonetic feature. Without contextual input, the performances collapse, but we propose for one model an original approach using an efficient non-linear data processing which leads to more simple algorithms and increases performances of the audiovisual fusion operator.