Novel Gaussianized vector representation for improved natural scene categorization

Pattern Recognition Letters

Volumn 31, Issue 8, 2010, Pages 702-708

  Zhou, Xi ^a   Zhuang, Xiaodan ^a   Tang, Hao ^a   Hasegawa Johnson, Mark ^a   Huang, Thomas S ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

Expectation Maximization; Gaussian mixture model; Image classification; Unsupervised learning

Indexed keywords

BAG OF FEATURES; COMPACT REPRESENTATION; EXPECTATION MAXIMIZATION; EXTENDED VERSIONS; GAUSSIAN COMPONENTS; GAUSSIAN MIXTURE MODEL; MULTINOMIAL DISTRIBUTIONS; MULTINOMIALS; NATURAL SCENES; SCENE CATEGORIZATION; SCENE IMAGE; SUPERVECTOR; UNSUPERVISED APPROACHES; VECTOR REPRESENTATIONS;

BLIND SOURCE SEPARATION; COMMUNICATION CHANNELS (INFORMATION THEORY); GAUSSIAN DISTRIBUTION; GRAPHIC METHODS; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE SEGMENTATION; MAXIMUM PRINCIPLE; MIXTURES; UNSUPERVISED LEARNING; VECTORS;

NORMAL DISTRIBUTION;

EID: 77950300605     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2009.12.010     Document Type: Article

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