We use features of segmentation for semantic classification of real images. We model the image in terms of a probability density function, a Gaussian mixture model (GMM) to be specific, of its region features. This GMM is fit to the image by adapting a universal GMM which is estimated so it fits all images.

Given an arbitrary image, our goal is to segment all distinct texture subimages. This is done by discovering distinct, cohesive groups of spatially repeating patterns, called texels, in the image, where each group defines the corresponding texture. Texels occupy image regions, whose photometric, geometric, structural, and spatial-layout properties are samples from an unknown pdf.

We propose novel approaches to region-based hierarchical image matching, where, given two images, the goal is to identify the largest part in image 1 and its match in image 2 having the maximum similarity measure defined in terms of geometric and photometric properties of regions (e.g., area, boundary shape, and color), as well as region topology (e.g.,

Low level segmentation based image features are used for the problem of object categorization. In general, object categorization comprises two main research areas: (1) classification or clustering of images containing objects belonging to an object category, and (2) detection, localization, and segmentation of individual object-category instances in images. The first thrust of research is typically concerned with exemplar based methods, where the main focus is to develop an efficient distance measure between two images.

Given a set of images, possibly containing objects from an unknown category, determine if a category is present. If a category is present, learn spatial and photometric model of the category. Given an unseen image, segment all occurrences of the category.

• S. Todorovic and N. Ahuja, Extracting Subimages of an Unknown Category from a Set of Images, Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New York, NY, Vol.

Faces and Gestures

The aforementioned work on representation and learning has contributed to two types of human computer interfaces we have developed. First, learning and classification techniques, including usual statistical classifiers, neural networks, support vector machines and artificial intelligence approaches, have been used to develop new methods for human face detection and hand gesture recognition.

Faces and Gestures

The aforementioned work on representation and learning has contributed to two types of human computer interfaces we have developed. First, learning and classification techniques, including usual statistical classifiers, neural networks, support vector machines and artificial intelligence approaches, have been used to develop new methods for human face detection and hand gesture recognition.

3D Object Recognition

Recognition is achieved either by explicitly coding the recognition criteria in terms of low level structure, or through learning from examples. Learning algorithms incorporate subspace projections of higher dimensional data symbolically or using neural approaches.

Learning to Recognize 3D Objects

A learning account for the problem of object recognition is developed within the PAC (Probably Approximately Correct) model of learnability.

3D Object Recognition

Recognition is achieved either by explicitly coding the recognition criteria in terms of low level structure, or through learning from examples. Learning algorithms incorporate subspace projections of higher dimensional data symbolically or using neural approaches.

Learning for Object Recognition

A learning algorithm accounting for the problem of object recognition is developed within the PAC (Probably Approximately Correct) model of learnability.

3D Object Recognition

Recognition is achieved either by explicitly coding the recognition criteria in terms of low level structure, or through learning from examples. Learning algorithms incorporate subspace projections of higher dimensional data symbolically or using neural approaches.

Learning of Low-level Spatiotemporal Structural Patterns

Given an image or a video sequence, a prespecified set of low level, spatial and/or temporal descriptors of the image/video structure, and a higher level interpretation of the structure, use computational learning methods to derive a succinct relationship between the interpretation and the low level structural description.

Faces and Gestures

The aforementioned work on representation and learning has contributed to two types of human computer interfaces we have developed. First, learning and classification techniques, including usual statistical classifiers, neural networks, support vector machines and artificial intelligence approaches, have been used to develop new methods for human face detection and hand gesture recognition.