JSM 2004 - Toronto

Abstract #301047

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Activity Number: 218
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Graphics
Abstract - #301047
Title: Learning the Linear Filters from Sounds and Images
Author(s): Ray-Bing Chen*+ and Ming Zheng
Companies: National University of Kaohsiung and University of California, Los Angeles
Address: Institute of Statistics, No. 700 Kaohsiung University Rd., Kaohsiung, International, 811, Taiwan, ROC
Keywords: filter linearing ; gradient method ; Markov chain Monte Carlo ; FRAME model
Abstract:

We are interesting in learning the linear filters from sounds and images. The model of sounds and images we considered here is FRAME model proposed by Zhu, Wu and Mumford (1997). After selecting the filters, FRAME model has good results in image analysis and synthesis. However, we assume the filters are all unknown and are learned from the data. In order to learn the filters, we propose an iterative algorithm that contents the two steps. The first step is to update the filters by gradient methods. Then the second step is to synthesize the signals based on the updating filters by MCMC methods. Finally, we demonstrate several experiments on sounds and images to show the performance of our algorithm.

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