Classification techniques

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Presentation transcript:

Classification techniques OLAM SE Petr Chmelař Lukáš Stryka Koncepty pro sledování chodců

Abstract My research aims to the knowledge discovery in multimedia data, especially mining visual data in large databases and its semantic description. My proposed contribution to the CPG project at FIT BUT is the application of corners and invariant region detectors, detection of surfaces and objects in addition to the proposed techniques, the comparison of classification techniques for hardware acceleration of recognition, tracking and augmented reality. Abstrakt anglicky

Projection of 3D objects’ surfaces into 2D Texture Analysis Projection of 3D objects’ surfaces into 2D Obrázek 3.2: Příklady textur z [Br66] – alba P. Brodatze

Texture analysis Texture synthesis Texture description (feature extraction) Classification (into known classes) Image segmentation (unknown number of classes) region-based boundary based Shape / object detection Many methods Statistical (Haralick) Signal processing (Gabor Filters) Geometry (Markov models) Efficient in image content indexing for content-based retrieval in large DBs.

Gabor Filters

DM applications Frequent set (association rules) application Irregularities, … noise: How to write such an algorithm?

Classification Theory of Information state class observation parameter estimation

Bayessian Classification Bayesian provides a method for adjusting degrees of belief of new information. likelihood prior information marginal probability P(y | xi)P(xi) posterior probability

Non-naïve Bayessian Classification Association analysis as a non-naïve Bayessian classification. a1  a2  …  ap  bp+1  …  bq (+ Support, Confidence, Correlation, Cover) maximum likelihood estimation maximum a posteriori

Find linear separation (hyper-plane): SVM Find linear separation (hyper-plane):

Kernels

Comparison MIT-CBCL face dataset (2 x 2500, Haar features)

Questions… ? Thank you. ctrl + S

References HAN, J., KAMBER, M. Data Mining: Concepts and Techniques. 2001. ISBN 1-55860-489-8. CRISITIANI, N. Kernel Methods for Pattern Analysis. 2002. http://www.support-vector.net/nello.html Please mail chmelarp@fit.vutbr.cz for more literature & self-citations.