Abstract book (pdf) - ICPR 2010

15:00-17:10, Paper MoBT9.38
Manifold Modeling with Learned Distance in Random Projection Space for Face Recognition
Tsagkatakis, Grigorios, Rochester Inst. of Tech.
Savakis, Andreas, Rochester Inst. of Tech.
In this paper, we propose the combination of manifold learning and distance metric learning for the generation of a representation
that is both discriminative and informative, and we demonstrate that this approach is effective for face recognition.
Initial dimensionality reduction is achieved using random projections, a computationally efficient and data independent
linear transformation. Distance metric learning is then applied to increase the separation between classes and improve the
accuracy of nearest neighbor classification. Finally, a manifold learning method is used to generate a mapping between
the randomly projected data and a low dimensional manifold. Face recognition results suggest that the combination of
distance metric learning and manifold learning can increase performance. Furthermore, random projections can be applied
as an initial step without significantly affecting the classification accuracy.
15:00-17:10, Paper MoBT9.39
Part Detection, Description and Selection based on Hidden Conditional Random Fields
Lu, Wenhao, Tsinghua Univ.
Wang, Shengjin, Tsinghua Univ.
Ding, Xiaoqing, Tsinghua Univ.
In this paper, the problem of part detection, description and selection is discussed. This problem is crucial in the learning
algorithms of part-based models, but cannot be solved well when some candidate parts are extracted from background.
This paper studies this problem and introduces a new algorithm, HCRF-PS (Hidden Conditional Random Fields for Part
Selection), for part detection, description, especially selection. Our algorithm is distinguished for its power to optimize
multiple kinds of information at the same time, including texture, color, location and part label. Finally, we did some experiments
with HCRF-PS algorithm which give good results on both virtual and real data.
15:00-17:10, Paper MoBT9.40
Boosting Bayesian MAP Classification
Piro, Paolo, CNRS/Univ. of Nice-Sophia Antipolis
Nock, Richard, Univ. des Antilles et de la Guyane
Nielsen, Frank, Ec. Pol.
Barlaud, Michel, CNRS/Univ. of Nice-Sophia Antipolis
In this paper we redefine and generalize the classic k-nearest neighbors (k-NN) voting rule in a Bayesian maximum-aposteriori
(MAP) framework. Therefore, annotated examples are used for estimating pointwise class probabilities in the
feature space, thus giving rise to a new instance-based classification rule. Namely, we propose to ``boost’’ the classic k-
NN rule by inducing a strong classifier from a combination of sparse training data, called ``prototypes’’. In order to learn
these prototypes, our MapBoost algorithm globally minimizes a multiclass exponential risk defined over the training data,
which depends on the class probabilities estimated at sample points themselves. We tested our method for image categorization
on three benchmark databases. Experimental results show that MapBoost significantly outperforms classic k-NN
(up to 8%). Interestingly, due to the supervised selection of sparse prototypes and the multiclass classification framework,
the accuracy improvement is obtained with a considerable computational cost reduction.
15:00-17:10, Paper MoBT9.41
Weighting of the K-Nearest-Neighbors
Chernoff, Konstantin, Univ. of Copenhagen
Nielsen, Mads
This paper presents two distribution independent weighting schemes for k-Nearest-Neighbors (kNN). Applying the first
scheme in a Leave-One-Out (LOO) setting corresponds to performing complete b-fold cross validation (b-CCV), while
applying the second scheme corresponds to performing bootstrapping in the limit of infinite iterations. We demonstrate
that the soft kNN errors obtained through b-CCV can be obtained by applying the weighted kNN in a LOO setting, and
that the proposed weighting schemes can decrease the variance and improve the generalization of kNN in a CV setting.
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