Abstract
Clustering the spatial feature of the object region plays an important role in object modeling, detecting and tracking etc. However, many clustering methods adopt the pre-set cluster number, which cannot adapt to full automatic system. In this paper, a novel frame work for adaptively determining the number of clusters is proposed based on hierarchically kernel cutting. We firstly extract the value contour of object region and rank the peaks of the contour in descending order. And then we utilize a group of gauss kernels located at peaks to sequentially segment the contour into several subintervals. When the residual area being not cut is lower than a threshold value, the cutting process is compulsively terminated. Furthermore, we merge adjacent kernels according to the intersection area ratio and take the retained kernel number as the cluster number k. We finally classify the object region with k and K-means algorithm. Both theoretical reasoning and experimental comparing illustrate the proposed method is rational, adaptive and efficient.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (61305011, 61703009), the Young Elite Scientist Sponsorship Program by China Association for Science and Technology (2017QNRC001), and Young Top-Notch Talents Team Program of Beijing Excellent Talents Funding (2017000026833ZK40). We thank the anonymous reviewers for their constructive comments.
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Lu, H., Gu, K., Yang, C., Hu, Y. (2019). Sequentially Cutting Based the Cluster Number Determination for Spatial Feature Classification. In: Zhai, G., Zhou, J., An, P., Yang, X. (eds) Digital TV and Multimedia Communication. IFTC 2018. Communications in Computer and Information Science, vol 1009. Springer, Singapore. https://doi.org/10.1007/978-981-13-8138-6_37
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DOI: https://doi.org/10.1007/978-981-13-8138-6_37
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