TY - GEN
T1 - Salient feature selection for visual concept learning
AU - Xu, Feng
AU - Zhang, Lei
AU - Zhang, Yu-Jin
AU - Ma, Wei-Ying
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2005
Y1 - 2005
N2 - Image classification could be treated as an effective solution to enable keyword-based semantic image retrieval. In this paper, we propose a novel image classification framework by learning semantic concepts of image categories. To choose representative features for an image category and meanwhile reduce noisy features, a three-step salient feature selection strategy is proposed. In the feature selection stage, salient patches are first detected and clustered. Then the region of dominance and salient entropy measures are calculated to reduce non-common salient patches for the category. Based on the selected visual keywords, SVM and keyword frequency model categorization method are applied to classification, respectively. The experimental results on Corel image database demonstrate that the proposed salient feature selection approach is very effective in image classification and visual concept learning. © Springer-Verlag Berlin Heidelberg 2005.
AB - Image classification could be treated as an effective solution to enable keyword-based semantic image retrieval. In this paper, we propose a novel image classification framework by learning semantic concepts of image categories. To choose representative features for an image category and meanwhile reduce noisy features, a three-step salient feature selection strategy is proposed. In the feature selection stage, salient patches are first detected and clustered. Then the region of dominance and salient entropy measures are calculated to reduce non-common salient patches for the category. Based on the selected visual keywords, SVM and keyword frequency model categorization method are applied to classification, respectively. The experimental results on Corel image database demonstrate that the proposed salient feature selection approach is very effective in image classification and visual concept learning. © Springer-Verlag Berlin Heidelberg 2005.
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U2 - 10.1007/11581772_54
DO - 10.1007/11581772_54
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 3540300279
SN - 9783540300274
VL - 3767 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 617
EP - 628
BT - Advances in Mulitmedia Information Processing - PCM 2005 - 6th Pacific Rim Conference on Multimedia, Proceedings
PB - Springer Verlag
T2 - 6th Pacific Rim Conference on Multimedia - Advances in Mulitmedia Information Processing - PCM 2005
Y2 - 13 November 2005 through 16 November 2005
ER -