Normalized Cuts and Image Segmentation

Normalized Cuts and Image Segmentation (2000)

by Jianbo Shi , Jitendra Malik

Venue:	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Citations:	1824 - 37 self

Datum	Value	Source
TITLE	Normalized Cuts and Image Segmentation	INFERENCE
AUTHOR NAME	Jianbo Shi	SVM HeaderParse 0.1
AUTHOR NAME	Jitendra Malik	SVM HeaderParse 0.1
ABSTRACT	AbstractÐWe propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmentation as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups. We show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion. We have applied this approach to segmenting static images, as well as motion sequences, and found the results to be very encouraging. Index TermsÐGrouping, image segmentation, graph partitioning. 1	SVM HeaderParse 0.1
YEAR	2000	INFERENCE
VENUE	IEEE Transactions on Pattern Analysis and Machine Intelligence	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	888--905	INFERENCE
VOLUME	22	INFERENCE
CITATIONS	25 found	ParsCit 1.0