Date: March 10, 2006

Time: 3:00 p.m.

Location: Centergy One 5186

Speaker(s): Dr. Thrasyvoulos Pappas
Electrical Engineering and Computer Science Department Northwestern University Evanston, Illinois

Title: Perceptual Image Segmentation and Semantic Classification

Abstract:
We present a new approach for semantic image analysis that combines knowledge of human perception with an understanding of signal characteristics to segment natural scenes into perceptually uniform regions, and then uses the region statistics to extract semantic information. Our focus is on images of natural scenes. One of the challenges of this problem is that the statistical characteristics of perceptually uniform regions are spatially-varying due to effects of lighting, perspective, scale changes, etc. We discuss a new adaptive perceptual color-texture segmentation algorithm that is based on two types of low-level features. The first describes the local color composition in terms of spatially adaptive dominant colors, and the second describes the spatial characteristics of the grayscale component of the texture. Key segmentation parameters are determined on the basis of subjective tests. The color and texture features of these regions are used as medium level descriptors, based on which we extract semantic labels, first at the segment and then at the scene level. The focus of this presentation is on region classification. We use a hierarchical vocabulary of segment labels that is consistent with those used in the NIST TRECVID 2003 development set. We test the approach on a database of 9000 segments obtained from 2500 photographs of natural scenes. For training and classification we use the Linear Discriminant Analysis (LDA) technique. We examine the performance of the algorithm (precision and recall rates) when different sets of features (e.g., one or two most dominant colors versus four quantized dominant colors) are used. Our results indicate that the proposed approach offers significant performance improvements over existing approaches.

Bio:
Thrasyvoulos (Thrasos) Pappas received the S.B., S.M., and Ph.D. degrees in electrical engineering and computer science from MIT in 1979, 1982, and 1987, respectively. From 1987 until 1999, he was a Member of the Technical Staff at Bell Laboratories, Murray Hill, NJ. In September 1999, he joined the Department of Electrical and Computer Engineering at Northwestern University as an associate professor. His research interests are in image and video quality and compression, perceptual models for image processing, model-based halftoning, image and video analysis, and multimedia signal processing. Dr. Pappas is a member of the Board of Governors of the Signal Processing Society of IEEE, and has served as chair of the IEEE Image and Multidimensional Signal Processing Technical Committee, associate editor of the IEEE Transactions on Image Processing, and technical program co-chair of ICIP-01 and the Symposium on Information Processing in Sensor Networks (IPSN-04). Since 1997 he has been co-chair of the SPIE/IS&T Conference on Human Vision and Electronic Imaging. He has also served as co-chair of the 2005 SPIE/IS&T Electronic Imaging Symposium. Dr. Pappas is a Fellow of the IEEE.

Date:	March 10, 2006
Time:	3:00 p.m.
Location:	Centergy One 5186
Speaker(s):	Dr. Thrasyvoulos Pappas Electrical Engineering and Computer Science Department Northwestern University Evanston, Illinois
Title:	Perceptual Image Segmentation and Semantic Classification
Abstract:	We present a new approach for semantic image analysis that combines knowledge of human perception with an understanding of signal characteristics to segment natural scenes into perceptually uniform regions, and then uses the region statistics to extract semantic information. Our focus is on images of natural scenes. One of the challenges of this problem is that the statistical characteristics of perceptually uniform regions are spatially-varying due to effects of lighting, perspective, scale changes, etc. We discuss a new adaptive perceptual color-texture segmentation algorithm that is based on two types of low-level features. The first describes the local color composition in terms of spatially adaptive dominant colors, and the second describes the spatial characteristics of the grayscale component of the texture. Key segmentation parameters are determined on the basis of subjective tests. The color and texture features of these regions are used as medium level descriptors, based on which we extract semantic labels, first at the segment and then at the scene level. The focus of this presentation is on region classification. We use a hierarchical vocabulary of segment labels that is consistent with those used in the NIST TRECVID 2003 development set. We test the approach on a database of 9000 segments obtained from 2500 photographs of natural scenes. For training and classification we use the Linear Discriminant Analysis (LDA) technique. We examine the performance of the algorithm (precision and recall rates) when different sets of features (e.g., one or two most dominant colors versus four quantized dominant colors) are used. Our results indicate that the proposed approach offers significant performance improvements over existing approaches.
Bio:	Thrasyvoulos (Thrasos) Pappas received the S.B., S.M., and Ph.D. degrees in electrical engineering and computer science from MIT in 1979, 1982, and 1987, respectively. From 1987 until 1999, he was a Member of the Technical Staff at Bell Laboratories, Murray Hill, NJ. In September 1999, he joined the Department of Electrical and Computer Engineering at Northwestern University as an associate professor. His research interests are in image and video quality and compression, perceptual models for image processing, model-based halftoning, image and video analysis, and multimedia signal processing. Dr. Pappas is a member of the Board of Governors of the Signal Processing Society of IEEE, and has served as chair of the IEEE Image and Multidimensional Signal Processing Technical Committee, associate editor of the IEEE Transactions on Image Processing, and technical program co-chair of ICIP-01 and the Symposium on Information Processing in Sensor Networks (IPSN-04). Since 1997 he has been co-chair of the SPIE/IS&T Conference on Human Vision and Electronic Imaging. He has also served as co-chair of the 2005 SPIE/IS&T Electronic Imaging Symposium. Dr. Pappas is a Fellow of the IEEE.