Year: 2004
Type: article
Abstract: 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.
Authors: Timothée Cour, Jianbo Shi
Institution: University of Pennsylvania
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1496525223