N. Jojic and B. J. Frey, "Video summarization and filtering using transformation-invariant hidden markov models," Submitted to International Journal on Computer Vision, 2001.  Home/Search   Document Not in Database   Summary   Related Articles

....jointly normalize out transformations that occur in training data, while learning a density model of the normalized data [3, 4] In this paper, we do not assume the data is ordered. Clearly, temporal coherence provides useful cues for modeling time series data such as video sequences [5 7] In [8 10], we show how the techniques introduced in this paper can be extended to discretestate dynamic models (hidden Markov models) One approach to data modeling and machine learning is to use labeled data to train a recognition model to accurately predict class membership from the input. This ....

....be used, such as rotation, scale, out of plane rotation and warping in images. Other domains may have quite different types of transformation. In the case of time series data, the transformations at the neighboring time steps influence which transformations are likely in the current time step. In [8 10], we show how the techniques presented here can be extended to time series. The number of computations needed for exact inference scales exponentially with the dimensionality of the transformation manifold. If there are n 1 transformations of the first type, n 2 transformations of the second ....

N. Jojic and B. J. Frey, "Video summarization and filtering using transformation-invariant hidden markov models," Submitted to International Journal on Computer Vision, 2001.

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