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Abstract:
Scene classification is a challenging problem in computer vision applications and can be used to model and analyze a special complex system, the internet community. The spatial PACT (Principal component Analysis of Census Transform histograms) is a promising representation for recognizing instances and categories of scenes. However, since the original spatial PACT only simply concatenates compact census transform histograms at all levels together, all levels have the same contribution, which ignores the difference among various levels. In order to ameliorate this point, we propose an adaptive multilevel kernel machine method for scene classification. Firstly, it computes a set of basic kernels at each level. Secondly, an effective adaptive weight learning scheme is employed to find the optimal weights for best fusing all these base kernels. Finally, support vector machine with the optimal kernel is used for scene classification. Experiments on two popular benchmark datasets demonstrate that the proposed adaptive multilevel kernel machine method outperforms the original spatial PACT. Moreover, the proposed method is simple and easy to implement.
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MATHEMATICAL PROBLEMS IN ENGINEERING
ISSN: 1024-123X
Year: 2013
Volume: 2013
ESI Discipline: ENGINEERING;
JCR Journal Grade:2
CAS Journal Grade:3
Cited Count:
WoS CC Cited Count: 0
SCOPUS Cited Count: 2
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 1
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