Artificial Neural Networks — ICANN'97: 7th International by Wolfram Schultz (auth.), Wulfram Gerstner, Alain Germond,

By Wolfram Schultz (auth.), Wulfram Gerstner, Alain Germond, Martin Hasler, Jean-Daniel Nicoud (eds.)

This ebook constitutes the refereed complaints of the seventh overseas convention on synthetic Neural Networks, ICANN'97, held in Lausanne, Switzerland,in October 1997. The 201 revised papers offered have been chosen from a great number of submissions and provides a different documentation of the cutting-edge within the region. The papers are geared up in elements on coding and studying in biology; cortical maps and receptive fields; studying: concept and functions; sign processing: blind resource separation, vector quantization, and self-organization; robotics, self sustaining brokers, and regulate; speech, imaginative and prescient and development reputation; prediction, forecasting and tracking; and implementations.

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4. M. J. Black and Y. Yacoob. Recognizing facial expressions under rigid and non-rigid facial motions. In International Workshop on Automatic Face and Gesture Recognition, Zurich, pages 12–17, 1995. 5. C. M. Omohundro. Nonlinear manifold learning for visual speech recognition. In Fifth International Conference on Computer Vision, pages 494–499, Boston, Jun 1995. 6. T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham. Active shape models — their training and application. In Computer vision, graphics and image understanding, volume 61, pages 38–59, 1995.

Xt−1 )}. (9) i 5 Combining Different Content Modalities So far we have not discussed in any detail what types of data can be modeled (k) by the the attributes Xt of equation (4). Because there is no constraint for these attributes to be of the same type, the Bayesian framework can naturally Learning over Multiple Temporal Scales 39 integrate many different modalities. In this work we restrict our attention to the integration of visual attributes with text annotations. Assuming a query Xt1 = {Tt1 , V1t }, composed of both text (Tt1 ) and visual attributes (V1t ), and using (9), and (4) g ∗(Xt1 ) = arg max{α log P(Vt |y = i)+α log P(Tt |y = i)+(1−α) log P(y = i|Xt−1 1 )} i (10) Disregarding the decay factor α, the comparison of this equation with (2) reveals an alternative interpretation for Bayesian integration: the optimal class is the one which would best satisfy the visual query alone but with a prior consisting of the combination of the second and third terms in the equation.

Some of those parts might correspond to an instance of the target object class (the foreground), while others stem from background clutter or are simply false detections (the background). Throughout this paper, the only information associated with an object part is its position in the image and its identity or part type. We assume that there are T different types of parts. The positions of all parts extracted from one image can be summarized in a matrix-like form,  x11 x12 , . . , x1N1  x21 x22 , .

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