Kernel-Spectral-Clustering-Driven Motion Segmentation: Rotating-Objects First Trials. Oña-Rocha, O., Riascos-Salas, J., A., Marrufo-Rodríguez, I., C., Páez-Jaime, M., A., Mayorca-Torres, D., Ponce-Guevara, K., L., Salazar-Castro, J., A., & Peluffo-Ordóñez, D., H. Communications in Computer and Information Science, pages 30-40. 2019. ![Communications in Computer and Information Science [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 1 download Time-varying data characterization and classification is a field of great interest in both scientific and technology communities. There exists a wide range of applications and challenging open issues such as: automatic motion segmentation, moving-object tracking, and movement forecasting, among others. In this paper, we study the use of the so-called kernel spectral clustering (KSC) approach to capture the dynamic behavior of frames - representing rotating objects - by means of kernel functions and feature relevance values. On the basis of previous research works, we formally derive a here-called tracking vector able to unveil sequential behavior patterns. As a remarkable outcome, we alternatively introduce an encoded version of the tracking vector by converting into decimal numbers the resulting clustering indicators. To evaluate our approach, we test the studied KSC-based tracking over a rotating object from the COIL 20 database. Preliminary results produce clear evidence about the relationship between the clustering indicators and the starting/ending time instance of a specific dynamic sequence.
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author = {Oña-Rocha, O. and Riascos-Salas, J. A. and Marrufo-Rodríguez, I. C. and Páez-Jaime, M. A. and Mayorca-Torres, D. and Ponce-Guevara, K. L. and Salazar-Castro, J. A. and Peluffo-Ordóñez, D. H.},
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chapter = {Kernel-Spectral-Clustering-Driven Motion Segmentation: Rotating-Objects First Trials},
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Downloads: 1
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