Abstract
The BoF (bag-of-features) model is one of the most famous models applied to many fields in computer vision and has achieved impressive results. However, the SIFT/HOG visual words have a limit discriminative power which is partly due to the fact that it only describes the local gradient distribution. In the meanwhile, there is still redundancy and hidden information existed in the formed histogram. Considering these respects, we propose a multi-modal SPM model which fuses global features to complement traditional local ones and conducts dimensionality reduction in local spaces for mining possible feature dependencies. Experimental results show the efficiency of the proposed method in comparison with the existing counterparts.
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Zheng, P., Zhao, ZQ., Gao, J. (2017). A Multi-modal SPM Model for Image Classification. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_46
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DOI: https://doi.org/10.1007/978-3-319-63315-2_46
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