Generic Object Recognition Using a Combination of ICA and Shape Cues

This paper addresses the problem of Generic Object Recognition by modeling the perceptual capability of human beings. In contrast to the traditional approaches, we have approached the recognition problem by proposing a framework which involves two stages of processing. First, an intelligent generic recognizer based on independent component analysis (ICA) is employed to reduce the search space to a few rank-ordered samples. It is shown that ICA captures the appearance characteristics of objects. Shape cues (distance transform based matching) are then used to verify the result of the appearance-based classifier and identify the correct object class and pose. Experiments were conducted using objects with complex appearance and shape characteristics. Sensitivity of recognition to the number of independent components and number of learning samples is analyzed on COIL-100 database. The performance of the generic classifier using ICA with and without shape matching is also analyzed.