Abstract
In this paper, a method of scene recognition based on multi feature fusion is proposed to solve the problems of poor accuracy in scene recognition of intelligent home robot. Firstly, the H/I color model is used to extract the color feature from the scene. Secondly, the characteristics of the uniform background are extracted by the DS descriptors, and the scene of great difference is extracted using the SURF descriptors. The extracted feature descriptors are quantized using the “visual bag of words”, and The SURF-DS-BOW model is generated by weighted fusion of the two feature descriptors. Finally, the multi kernel learning support vector machine (MKL-SVM) is used to fuse the color feature and the SURF-DS-BOW model to improve the accuracy of scene recognition. The experimental results show that the recognition rate of the method in indoor scene recognition is 86.4%, which is better than the relevant literature algorithm.
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Liu, X., Hong, W., Lu, H. (2017). Scene Recognition Based on Multi-feature Fusion for Indoor Robot. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_17
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DOI: https://doi.org/10.1007/978-3-319-70090-8_17
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