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Learning object recognition strategies
Abstract
Most knowledge-directed vision systems recognize objects by the use of hand-crafted, heuristic control strategies. Generally, the programmer or knowledge engineer who constructs them begins with an intuitive notion of how an object should be recognized, a notion that is laboriously refined by trial-and-error. Eventually the programmer finds a combination of features (e.g. shape, color, or context) and methods (e.g. geometric model matching, minimum-distance classification or generalized Hough transforms) that allow each object to be reliably identified within its domain. Unfortunately, human engineering is not cost-effective for many real-world applications, a defect that has relegated most knowledge-directed visions systems to the laboratory. Knowledge-directed systems also tend to be difficult to analyze, since their performance, in terms of cost, accuracy, and reliability, is unknown, and comparisons to other hand-crafted systems are difficult at best. Worst of all, when the domain is changed, knowledge-directed systems often have to be rebuilt from scratch. The Schema Learning System (SLS) addresses these problems by learning knowledge-directed recognition strategies under supervision. More precisely, SLS learns its recognition strategies from training images (with solutions) and a library of generic visual procedures. The result is a system that develops robust and efficient recognition strategies with a minimum of human involvement, and that analyzes the strategies it learns to estimate both their expected cost and probability of failure. In order to represent strategies, recognition is modeled in SLS as a sequence of small verification tasks interleaved with representational transformations. At each level of representation, features of a representational instance, called a hypothesis, are measured in order to verify or reject the hypothesis. Hypotheses that are verified (or, more accurately, not rejected) are then transformed to a more abstract level of representation, where features of the new representation are measured and the process repeats itself. The recognition graphs learned by SLS are executable recognition graphs capable of recognizing the 3D locations and orientations of objects in complex scenes.
Subject Area
Computer science|Artificial intelligence
Recommended Citation
Draper, Bruce Anthony, "Learning object recognition strategies" (1993). Doctoral Dissertations Available from Proquest. AAI9329597.
https://scholarworks.umass.edu/dissertations/AAI9329597