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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.
segmentation, recognition, learning spectral graph, derivative of eigenvectors, normalized cuts
Timothée Cour and Jianbo Shi, "A Learnable Spectral Memory Graph for Recognition and Segmentation", . June 2004.
Date Posted: 25 May 2005