Download e-book for kindle: Advanced Concepts for Intelligent Vision Systems: 13th by David Van Hamme, Peter Veelaert, Wilfried Philips (auth.),

By David Van Hamme, Peter Veelaert, Wilfried Philips (auth.), Jacques Blanc-Talon, Richard Kleihorst, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)

ISBN-10: 3642236863

ISBN-13: 9783642236860

ISBN-10: 3642236871

ISBN-13: 9783642236877

This ebook constitutes the refereed complaints of the thirteenth overseas convention on complex options for clever imaginative and prescient structures, ACIVS 2011, held in Ghent, Belgium, in August 2011.

The sixty six revised complete papers offered have been conscientiously reviewed and chosen from 124 submissions. The papers are prepared in topical sections on class attractiveness, and monitoring, segmentation, pictures research, photo processing, video surveillance and biometrics, algorithms and optimization; and 3D, intensity and scene understanding.

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Read or Download Advanced Concepts for Intelligent Vision Systems: 13th International Conference, ACIVS 2011, Ghent, Belgium, August 22-25, 2011. Proceedings PDF

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Additional resources for Advanced Concepts for Intelligent Vision Systems: 13th International Conference, ACIVS 2011, Ghent, Belgium, August 22-25, 2011. Proceedings

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Let Cf (W, V ) = k j=1 n i=1 wi,j d(fi , vj ) Cy (W, U ) = k j=1 n i=1 wi,j δ(fi , vj ) Then C(W, V, U ) = k j=1 n i=1 wi,l d (fi , pj ) can be written as C(W, V, U ) = Cf (W, V ) + λCy (W, U ). Since Cf (W, V ) and Cy (W, U ) are nonnegative and independent of each other, minimizing C(W, V, U ) respect to V , U is equivalent to minimizing Cf (W, V ) and Cy (W, U ) with respect to V and U respectively. It is obvious that Cf (W, V ) is minimized when (7) holds, which is the same as that in the standard k-means.

Repeat steps 2 and 3 until there is no changes for each cluster. The vocabulary constructed by k-means solely considers the statistics of local image patches without taking category information into account. Then it cannot capture the semantic relation between image features. As discussed above, this vocabulary is not optimal for classification tasks. Inspired by [12], we propose a technique to construct a supervised visual vocabulary by jointly considering image features and their class labels.

The supervised vocabulary method consistently outperforms the vocabulary obtained using k-means for both classifiers. Moreover, the computation time of the supervised vocabulary generation is very close to that of k-means. 78s for k-means. Fig. 3. Classification accuracy (%) plotted with λ Table 1. 2 OT Dataset OT dataset contains 8 categories, {coast, forest, highway, insidecity, mountain, opencountry, street, tallbuilding}. We use the first 200 images per class in the experiment. Images within each class are randomly divided into two subsets of the same size to form a training set and a testing set.

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Advanced Concepts for Intelligent Vision Systems: 13th International Conference, ACIVS 2011, Ghent, Belgium, August 22-25, 2011. Proceedings by David Van Hamme, Peter Veelaert, Wilfried Philips (auth.), Jacques Blanc-Talon, Richard Kleihorst, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)


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