A self-organizing framework for object recognition is described. We describe a hierarchical database structure for image retrieval. The self-organizing hierarchical optimal subspace learning and inference framework (SHOSLIF) system uses the theories of optimal linear projection for optimal feature derivation and a hierarchical structure to achieve logarithmic retrieval complexity. A space-tessellation tree is generated using the most expressive features (MEF) and most discriminating features (MDF) at each level of the tree. The major characteristics of the analysis include: (1) avoiding the limitation of global linear features by deriving a recursively better-fitted set of features for each of the recursively subdivided sets of training samples; (2) generating a smaller tree whose cell boundaries separate the samples along the class boundaries better than the principal component analysis, thereby giving a better generalization capability (i.e., better recognition rate in a disjoint test); (3) accelerating the retrieval using a tree structure for data pruning, utilizing a different set of discriminant features at each level of the tree. We allow for perturbations in the size and position of objects in the images through learning. We demonstrate the technique on a large image database of widely varying real-world objects taken in natural settings, and show the applicability of the approach for variability in position, size, and 3D orientation. This paper concentrates on the hierarchical partitioning of the feature spaces.

The method we have been using is based on our Self-Organizing Hierarchical Optimal Subspace Learning and Inference Framework (SHOSLIF). It uses the theories of linear discriminant projection for automatic optimal feature selection in each of the internal nodes of a Space-Tessellation Tree. In this paper, we present our recent study on the applicability of the approach to variability in position, size, and 3D orientation. In the work presented here, we require "well-framed" images os input for recognition. By well-framed images we mean that only a relatively small variation in the size, position, and orientation of the objects in the input images is allowed. We report the experimental results that show the performance difference between the subspaces of linear discriminant analysis and the principle component analysis and the effect of using a tree as opposed to a flat eigenspace.

This paper describes the automatic selection of features from an image training set using the theories of multidimensional discriminant analysis and the associated optimal linear projection. We demonstrate the effectiveness of these most discriminating features for view-based class retrieval from a large database of widely varying real-world objects presented as "well-framed" views, and compare it with that of the principal component analysis.

We present a self-organizing framework called the SHOSLIF-M for learning and recognizing spatiotemporal events (or patterns) from intensity image sequences. The proposed framework consists of a multiclass, multivariate discriminant analysis to automatically select the most discriminating features (MDF), a space partition tree to achieve a logarithmic retrieval time complexity for a database of n items, and a general interpolation scheme to do view inference and generalization in the MDF space based on a small number of training samples. The system is tested to recognize 28 different hand signs. The experimental results show that the learned system can achieve a 96% recognition rate for test sequences that have not been used in the training phase.>

A realworld computer vision module must deal with a wide variety of environmental parameters. Object recognition, one of the major tasks of this vision module, typically requires a preprocessing step to locate objects in the scenes that ought to be recognized. Genetic algorithms are a search technique for dealing with a very large search space, such as the one encountered in image segmentation or object recognition. The article describes a technique for using genetic algorithms to combine the image segmentation and object recognition steps for a complex scene. The results show that this approach is a viable method for successfully combining the image segmentation and object recognition steps for a computer vision module.