msri workshop, january 2005 object recognition collected databases of objects on uniform background...
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MSRI workshop, January 2005
Object Recognition • Collected databases of objects on uniform background
(no occlusions, no clutter)• Mostly focus on viewpoint variations (at fixed scale)
• PCA based techniques, Eigenimages [Turk 91], Leonardis & Bishoph 98]• Not robust with respect to • occlusions, clutter changes of viewpoint• Requires segmentation
MSRI workshop, January 2005
History - Recognition
• Alternative descriptors - Color histogram [Swain 91] (not discriminative enough)
• Geometric invariants [Rothwell 92] - Function with a value independent of the transformation - Invariant for image rotation : distance of two points - Invariant for planar homography : cross-ratio
MSRI workshop, January 2005
Figure from “Efficient model library access by projectively invariant indexing functions,” by C.A. Rothwell et al., Proc. Computer Vision and Pattern Recognition, 1992, copyright 1992, IEEE - (courtesy Forsythe, Ponce CV, Prentice Hall)
MSRI workshop, January 2005
Recognition with local photometric invariants
[ Local greyvalue invariants for image retrieval, C. Schmid and R. Mohr, PAMI 1997 ]
> 5000images
Semi-local constraints, neighboring points should match, angles, lengthratios should be similar
MSRI workshop, January 2005
Object Recognition - Present
• Can representations of objects be learned automatically • Given datasets of images containing unsegmented objects
• Which objects can be recognized in images ? • Which object parts are distinctive ? • What are the parameters of global shape or geometry ?
MSRI workshop, January 2005
Figure from, “A general framework for object detection,” by C. Papageorgiou, M. Oren and T. Poggio, Proc. ICCV, copyright 1998, IEEE
Recognition by finding patternsGeneral strategy:
search image windows at a range of scales correct for illuminationPresent corrected window to classifier: - face/no face classifier
Figure from A Statistical Method for 3D Object Detection Applied to Faces and Cars, H. Schneiderman and T. Kanade, CVPR, 2000.
MSRI workshop, January 2005
Constellation of Parts Model
Fischler & Elschlagar, 1973
• [Coots, Taylor et al’95] Univ. of Manchester• [G.Csurka et al’04] Xerox Research Europe• [Webber et al’00, Fei-Fei Li et al 03] Caltech• [Fergus et al’03,04] Oxford
• Constellation of parts models• Object detection part, category recognition
MSRI workshop, January 2005
Constellation of Parts Model• Strategy for learning models for recognition
Idea: Learn generative probabilistic model of objects
1. Run part detectors, obtain parts (location, appearance, scale)
2. Form likely object hypothesis, update the probability model and validate - hypothesis is particular configuration of parts
Recognition (computing likelihood ratio)
MSRI workshop, January 2005
Foreground model
Gaussian shape pdf
Prob. of detection
Gaussian part appearance pdf
Generative probabilistic model courtesy of R. Fergus (presentation)
Uniform shape pdf
Clutter model
Gaussian appearance pdf
Gaussian relative scale pdf
Log(scale)
0.8 0.75 0.9
Poission pdf on # detections
Uniformrelative scale pdf
Log(scale)
MSRI workshop, January 2005
Constellation model and Bayesian Framework
• P parts: location X, Scale S and Appearance A.
• Distribution is modeled by hidden parameters .( e.g. Mean, Covariance of Gaussian)
• Maximum Likelihood (ML) with a single value (Fergus et al) . • Approximation to make the integral tractable (Li Fei-Fei et al)
Appearance, shape, scale, hypothesis
MSRI workshop, January 2005
Motorbikes (Fergus’s results)
Samples from appearance model
MSRI workshop, January 2005
References
• L. Fei-Fei, R. Fergus, and P. Perona. A Bayesian approach to unsupervised One-Shot learning of Object categories. Proc. ICCV. 2003
• Fergus, R. , Perona, P. and Zisserman, A., A Visual Category Filter for Google Images ,Proc. Of of the 8th European Conf. on Computer Vision, ECCV 2004.
• Y. Amit and D. Geman, “A computational model for visual selection”, Neural Computation, vol. 11, no. 7, pp1691-1715, 1999.
• M. Weber, M. Welling and P. Perona, “Unsupervised learning of models for recognition”, Proc. 6th ECCV, vol. 2, pp. 101-108, 2000.
MSRI workshop, January 2005
Recognition …
• Integration of multiple view models (Complex 3D objects)• Generative vs Discriminative Models• Scaling issues > 10000 object • Recognition of object categories
• Alternative models of context• intra-object-within-class variations (chairs)• Different feature types • Enable models with large number of parts
• Image based retrieval – annotating by semantic context• Associating words with pictures