Developing dually optimal LCA features in sensory and action spaces for classification

Appearance based methods have utilized a variety of techniques such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-means clustering, and sparse autoencoders to extract training-data dependent features from static images - by static images, we mean, foreground objects and backgrounds are present in static images as snapshots. The Developmental Networks (DN) use Lobe Component Analysis (LCA) features developed not only from the image space X but also the action space Z. Since, Z can be taught to represent a set of trainer specified meanings (e.g., type and location), a DN treats these meanings in a unified way for both detection and recognition for objects in dynamic cluttered backgrounds. However, the DN method has not been applied to publicly available datasets and compared with well-known major techniques. In this work, we fill this void. We describe how the Z information enables the features to be more sensitive to trainer specified output meanings (e.g., type and location). The reported experiments fall into two extensively studied categories - global template based object recognition and local template based scene classification. For the datasets used, the performance of the DN method is better or comparable to some major local template based methods but the DNs also provide statistics-based location information about the object in a cluttered scene.