Abstract book (pdf) - ICPR 2010

10:00-10:20, Paper ThAT2.4
Rectifying Non-Euclidean Similarity Data using Ricci Flow Embedding
Xu, Weiping, Univ. of York
Hancock, Edwin, Univ. of York
Wilson, Richard, Univ. of York
Similarity based pattern recognition is concerned with the analysis of patterns that are specified in terms of object dissimilarity
or proximity rather than ordinal values. For many types of data and measures, these dissimilarities are not Euclidean.
This hinders the use of many machine-learning techniques. In this paper, we provide a means of correcting or rectifying
the similarities so that the non-Euclidean artifacts are minimized. We consider the data to be embedded as points on a
curved manifold and then evolve the manifold so as to increase its flatness. Our work uses the idea of Ricci flow on the
constant curvature Riemannian manifold to modify the Gaussian curvatures on the edges of a graph representing the non-
Euclidean data. We demonstrate the utility of our method on the standard ``Chicken pieces’’ dataset and show that we can
transform the non-Euclidean distances into Euclidean space.
10:20-10:40, Paper ThAT2.5
One-Vs-All Training of Prototype Classifier for Pattern Classification and Retrieval
Liu, Cheng-Lin, Chinese Acad. of Sciences
Prototype classifiers trained with multi-class classification objective are inferior in pattern retrieval and outlier rejection.
To improve the binary classification (detection, verification, retrieval, outlier rejection) performance of prototype classifiers,
we propose a one-vs-all training method, which enriches each prototype as a binary discriminant function with a local
threshold, and optimizes both the prototype vectors and the thresholds on training data using a binary classification objective,
the cross-entropy (CE). Experimental results on two OCR datasets show that prototype classifiers trained by the onevs-all
method is superior in both multi-class classification and binary classification.
ThAT3 Topkapı Hall A
Computer Vision Applications - I Regular Session
Session chair: Haindl, Michael (Institute of Information Theory)
09:00-09:20, Paper ThAT3.1
Probabilistic Modeling of Dynamic Traffic Flow across Non-Overlapping Camera Views
Huang, Ching-Chun, National Chiao Tung University
Chiu, Wei-Chen, Department of Computer Science
Wang, Sheng-Jyh, National Chiao Tung Univ.
Chuang, Jen-Hui, National Chiao Tung Univ.
In this paper, we propose a probabilistic method to model the dynamic traffic flow across non-overlapping camera views.
By assuming the transition time of object movement follows a certain global model, we may infer the time-varying traffic
status in the unseen region without performing explicit object correspondence between camera views. In this paper, we
model object correspondence and parameter estimation as a unified problem under the proposed Expectation-Maximization
(EM) based framework. By treating object correspondence as a latent random variable, the proposed framework can iteratively
search for the optimal model parameters with the implicit consideration of object correspondence.
09:20-09:40, Paper ThAT3.2
Vehicle Recognition as Changes in Satellite Imagery
Ozcanli, Ozge Can, Brown Univ.
Mundy, Joseph,
Over the last several years, a new probabilistic representation for 3-d volumetric modeling has been developed. The main purpose of the
model is to detect deviations from the normal appearance and geometry of the scene, i.e. change detection. In this paper, the model is
utilized to characterize changes in the scene as vehicles. In the training stage, a compositional part hierarchy is learned to represent the
geometry of Gaussian intensity extrema primitives exhibited by vehicles. In the test stage, the learned compositional model produces vehicle
detections. Vehicle recognition performance is measured on low-resolution satellite imagery and detection accuracy is significantly improved
over the initial change map given by the 3-d volumetric model. A PCA-based Bayesian recognition algorithm is implemented for comparison,
which exhibits worse performance than the proposed method.
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