Abstract
Automatic image scene detection is a crucial step for various tasks in computer vision. Current scene detection methods are often computationally expensive for use in real-time image classification. In this paper, a novel and efficient scene detection method based on local invariant features is presented. First, the SIFT feature detector and descriptor has been utilized to extract local image features since the SIFT descriptor has been proved to be an excellent local method that yields high quality features. However, the SIFT descriptor has been shown to produce high dimensional and redundant local features, which can create processing difficulty and computational burden in the successive classification stage. Therefore, two new feature selection strategies are proposed to reduce the number of SIFT keypoints and hence reduce the computational complexity. In both strategies, each image is represented by a single feature vector which assures the efficiency. Finally, a multi-classifier based on a support vector machine is applied to perform the scene detection task. Experimental results show that the proposed method can achieve accurate satisfactory classification results with significantly reduced computational complexity.
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Acknowledgments
This work has been supported by the National Natural Science Foundation of China under contract No. 61201337 and by Changsha Municipal Science and Technology Project under contract No. K1205045-11. The authors are grateful to the anonymous reviewers for valuable comments.
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Xie, Y., Zhang, XP., Luan, X. et al. A novel specific image scenes detection method. Multimed Tools Appl 74, 105–122 (2015). https://doi.org/10.1007/s11042-013-1496-7
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DOI: https://doi.org/10.1007/s11042-013-1496-7