Incremental learning of temporally-coherent Gaussian mixture models
Arandjelovic, Ognjen and Cipolla, R. 2006, Incremental learning of temporally-coherent Gaussian mixture models, Society of Manufacturing Engineers (SME) Technical Papers, vol. TP06PUB22, pp. 1-1.
In this paper we address the problem of learning Gaussian Mixture Models (GMMs) incrementally. Unlike previous approaches which universally assume that new data comes in blocks representable by GMMs which are then merged with the current model estimate, our method works for the case when novel data points arrive one- by-one, while requiring little additional memory. We keep only two GMMs in the memory and no historical data. The current fit is updated with the assumption that the number of components is fixed which is increased (or reduced) when enough evidence for a new component is seen. This is deducedfrom the change from the oldest fit of the same complexity, termed the Historical GMM, the concept of which is central to our method. The performance of the proposed method is demonstrated qualitatively and quantitatively on several synthetic data sets and video sequences of faces acquired in realistic imaging conditions.
Language
eng
Field of Research
080104 Computer Vision 080106 Image Processing 080109 Pattern Recognition and Data Mining
Socio Economic Objective
890205 Information Processing Services (incl. Data Entry and Capture)
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