Abstract book (pdf) - ICPR 2010

10:20-10:40, Paper ThAT1.5
A Re-Evaluation of Pedestrian Detection on Riemannian Manifolds
Tosato, Diego, Univ. of Verona
Farenzena, Michela, Univ. of Verona
Cristani, Marco, Univ. of Verona
Murino, Vittorio, Univ. of Verona
Boosting covariance data on Riemannian manifolds has proven to be a convenient strategy in a pedestrian detection context.
In this paper we show that the detection performances of the state-of-the-art approach of Tuzel et al. [7] can be greatly improved,
from both a computational and a qualitative point of view, by considering practical and theoretical issues, and
allowing also the estimation of occlusions in a fine way. The resulting detection system reaches the best performance on the
INRIA dataset, setting novel state-of-the art results.
ThAT2 Anadolu Auditorium
Classification - I Regular Session
Session chair: Duin, Robert (TU Delft)
09:00-09:20, Paper ThAT2.1
An Optimum Class-Rejective Decision Rule and its Evaluation
Le Capitaine, Hoel, Univ. of La Rochelle
Frelicot, Carl, Univ. of La Rochelle
Decision-making systems intend to copy human reasoning which often consists in eliminating highly non probable situations
(e.g. diseases, suspects) rather than selecting the most reliable ones. In this paper, we present the concept of class-rejective
rules for pattern recognition. Contrary to usual reject option schemes where classes are selected when they may correspond
to the true class of the input pattern, it allows to discard classes that can not be the true one. Optimality of the rule is proven
and an upper-bound for the error probability is given. We also propose a criterion to evaluate such class-rejective rules. Classification
results on artificial and real datasets are provided.
09:20-09:40, Paper ThAT2.2
A Practical Heterogeneous Classifier for Relational Databases
Manjunath, Geetha, Indian Inst. of Science
M, Narasimha Murty, Indian Inst. of Science
Sitaram, Dinkar, Hewlett Packard Company
Most enterprise data is distributed in multiple relational databases with expert-designed schema. Using traditional singletable
machine learning techniques over such data not only incur a computational penalty for converting to a flat form (megajoin),
even the human-specified semantic information present in the relations is lost. In this paper, we present a two-phase
hierarchical meta-classification algorithm for relational databases with a semantic divide and conquer approach. We propose
a recursive, prediction aggregation technique over heterogeneous classifiers applied on individual database tables. A preliminary
evaluation on TPCH and UCI benchmarks shows reduced training time without any loss of prediction accuracy.
09:40-10:00, Paper ThAT2.3
Spatial Representation for Efficient Sequence Classification
Kuksa, Pavel, Rutgers Univ.
Pavlovic, Vladimir, Rutgers Univ.
We present a general, simple feature representation of sequences that allows efficient inexact matching, comparison and
classification of sequential data. This approach, recently introduced for the problem of biological sequence classification,
exploits a novel multi-scale representation of strings. The new representation leads to discovery of very efficient algorithms
for string comparison, independent of the alphabet size. We show that these algorithms can be generalized to handle a wide
gamut of sequence classification problems in diverse domains such as the music and text sequence classification. The presented
algorithms offer low computational cost and highly scalable implementations across different application domains.
The new method demonstrates order-of-magnitude running time improvements over existing state-of-the-art ap
proaches while matching or exceeding their predictive accuracy.
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