Toward learning visual discrimination strategies
Humans learn strategies for visual discrimination through interaction with their environment. Discrimination skills are refined as demanded by the task at hand, and are not a priori determined by any particular feature set. Tasks are typically incompletely specified and evolve continually. This work presents a general framework for learning visual discrimination that addresses some of these characteristics. It is based on an infinite combinatorial feature space consisting of primitive features such as oriented edgels and texture signatures, and compositions thereof. Features are progressively sampled from this space in a simple-to-complex manner. A simple recognition procedure queries learned features one by one and rules out candidate object classes that do not sufficiently exhibit the queried feature. Training images are presented sequentially to the learning system, which incrementally discovers features for recognition. Experimental results on two databases of geometric objects illustrate the applicability of the framework.