A Learnable Spectral Memory Graph for Recognition and Segmentation

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Technical Reports (CIS)
General Robotics, Automation, Sensing and Perception Laboratory
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GRASP
segmentation
recognition
learning spectral graph
derivative of eigenvectors
normalized cuts
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Cour, Timothée
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Image segmentation is often treated as an unsupervised task. Segmentation by human, in contrast, relies heavily on memory to produce an object-like clustering, through a mechanism of controlled hallucination. This paper presents a learning algorithm for memory-driven object segmentation and recognition. We propose a general spectral graph learning algorithm based on gradient descent in the space of graph weight matrix using derivatives of eigenvectors. The gradients are efficiently computed using the theory of implicit functions. This algorithm effectively learns a graph network capable of memorizing and retrieving multiple patterns given noisy inputs. We demonstrate the validity of this approach on segmentation and recognition tasks, including geometric shape extraction, and hand-written digit recognition.

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2004-06-01
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University of Pennsylvania Department of Computer and Information Science Technical Report No. MS-CIS-04-12.
<br>University of Pennsylvania Department of Computer and Information Science Technical Report No. MS-CIS-04-12.<br><p>
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