ITR: Learning and recognition of objects in sensory data.

California Institute Of Technology, Pasadena CA

Investigators

Abstract

Humans can recognize objects and scenes using their senses. The ability of learning the appearance of a great number of objects, organizing them into categories, and quickly recognizing them later is an important skill for survival. Replicating such ability in machines would be extremely useful in a great number of scientific and industrial applications such as automatic exploration of databases of medical images, diagnostics and quality control in industrial plants, automatic classification of images and sounds on the web. The aim of this study is to develop a theory of recognition that is applicable any type of sensory data and where no supervision is required for learning and categorization. The approach is probabilistic: object categories are modeled by probability density functions on part appearance and object shape. Detection and recognition are formulated as statistical inference problems. Unsupervised learning of object categories is approached using maximum likelihood. In order to motivate and test the theory the investigators will engage in three applications: automatic classification and retrieval of objects from image databases, of human actions from movies, and of neuronal signals associated with perceptual tasks.