Independent Component Analysis Filtering Research Articles

Learning Midlevel Image Features for Natural Scene and Texture Classification

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

Independent component analysis for texture segmentation

Independent component analysis (ICA) of textured images is presented as a computational technique for creating a new data dependent filter bank for use in texture segmentation. We show that the ICA filters are able to capture the inherent properties of textured images. The new filters are similar to Gabor filters, but seem to be richer in the sense that their frequency responses may be more complex. These properties enable us to use the ICA filter bank to create energy features for effective texture segmentation. Our experiments using multi-textured images show that the ICA filter bank yields similar or better segmentation results than the Gabor filter bank.

Saliency map model with adaptive masking based on independent component analysis

We propose a new saliency map model with an adaptive masking method. Independent component analysis (ICA) is used to find a filter that can generate a saliency point from a color natural scene. Also, we consider the roles of cells in retina such as edge detection and color opponency as a preprocessor of an ICA filter that models the brain function to reduce the redundancy. In order to adopt a suitable masking in a salient point, a noise-tolerant generalized symmetry transformation is incorporated into the morphology operation. Computer experimental results show that the proposed model successfully generates the sequence of salient points.

High order statistics for image classification.

This paper addresses the problem of image classification using local information which is aggregated to provide global representation of different image classes. Local information is adaptively extracted from an image database using Independent Component Analysis (ICA) which provides a set of localized, oriented, and band-pass filters selective to independent features of the images. Local representation using ICA techniques has been previously investigated by several researchers. However, very little work has been done on further use of these representations to provide more complex and global description of images. In this paper, we present an algorithm which uses the energy of a minimal set of ICA filters to provide class-specific signatures which are shown to be strongly discriminant. Computer simulations are carried on two image databases, one consisting of five classes--referred to as categories--(buildings, rooms, mountains, forests and beaches) and one consisting of a set of 30 objects from multiple views for viewpoint invariant object recognition. The classification performance of the algorithm using both Independent and Principal Component Analyses are reported and discussed.