An algorithm selection based platform for image understanding using high-level symbolic feedback and machine learning

Abstract

Natural image processing and understanding encompasses hundreds of different algorithms. Each algorithm generates best results for a particular set of input features and configurations of the objects/regions in the input image (environment). To obtain the best possible result of processing in a reliable manner, we propose an algorithm selection approach that selects the best algorithm for a each input image. The proposed algorithm selection starts by first selecting an algorithm using low level features such as color intensity, histograms, spectral coefficients or so and a user given context if available. The resulting high-level image description is analyzed for logical inconsistencies (contradictions) and image regions that must be processed using a different algorithm are selected. The high-level description and the optional user-given context are used by a Bayesian Network to estimate the cause of the error in the processing. The same Bayesian Network also generates new candidate algorithm for each region containing the contradiction in an iterative manner. This iterative selection stops when the high-level inconsistencies are all resolved or no more different algorithms can be selected. We also show that when inconsistencies can be detected, our framework is able to improve high-level description when compared with single algorithms. In order for such complex and iterative processing being computationally tractable we also introduce a hardware platform based on reconfigurable VLSI that is well suited as the platform of the proposed approach. We show that the algorithm selected approach is ideally suited for either a hybrid type VLSI processor or for a Logic-In-Memory processing platform.