CASIA OpenIR  > 09年以前成果
Multiple similarities based kernel subspace learning for image classification
Yan, W; Liu, QS; Lu, HQ; Ma, SD; Narayanan, PJ; Nayar, SK; Shum, HY
Source PublicationCOMPUTER VISION - ACCV 2006, PT II
2006
Volume3852Pages:244-253
SubtypeArticle
AbstractIn this paper, we propose a new method for image classification, in which matrix based kernel features are designed to capture the multiple similarities between images in different low-level visual cues. Based on the property that dot product kernel can be regarded as a similarity measure, we apply kernel functions to different low-level visual features respectively to measure the similarities between two images, and obtain a kernel feature matrix for each image. In order to deal with the problems of over fitting and numerical computation, a revised version of Two-Dimensional PCA algorithm is developed to learn intrinsic subspace of matrix features for classification. Extensive experiments on the Corel database show the advantage of the proposed method.
WOS HeadingsScience & Technology ; Technology
WOS KeywordHUMAN FACES
Indexed ByISTP ; SCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS IDWOS:000235773200025
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9229
Collection09年以前成果
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Yan, W,Liu, QS,Lu, HQ,et al. Multiple similarities based kernel subspace learning for image classification[J]. COMPUTER VISION - ACCV 2006, PT II,2006,3852:244-253.
APA Yan, W.,Liu, QS.,Lu, HQ.,Ma, SD.,Narayanan, PJ.,...&Shum, HY.(2006).Multiple similarities based kernel subspace learning for image classification.COMPUTER VISION - ACCV 2006, PT II,3852,244-253.
MLA Yan, W,et al."Multiple similarities based kernel subspace learning for image classification".COMPUTER VISION - ACCV 2006, PT II 3852(2006):244-253.
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