Abstract book (pdf) - ICPR 2010

transformed into a polar histogram. Finally, we use these local descriptors to detect and locate similar objects within any
images. The experiment results show that the proposed method outperforms the existing state-of-the-art work.
10:20-10:40, Paper TuAT1.5
Inverse Multiple Instance Learning for Classifier Grids
Sternig, Sabine, Graz Univ. of Tech.
Roth, Peter M., Graz Univ. of Tech.
Bischof, Horst, Graz Univ. of Tech.
Recently, classifier grids have shown to be a considerable alternative for object detection from static cameras. However,
one drawback of such approaches is drifting if an object is not moving over a long period of time. Thus, the goal of this
work is to increase the recall of such classifiers while preserving their accuracy and speed. In particular, this is realized
by adapting ideas from Multiple Instance Learning within a boosting framework. Since the set of positive samples is well
defined, we apply this concept to the negative samples extracted from the scene: Inverse Multiple Instance Learning. By
introducing temporal bags, we can ensure that each bag contains at least one sample having a negative label, providing
the required stability. The experimental results demonstrate that using the proposed approach state-of-the-art detection results
can by obtained, however, showing superior classification results in presence of non-moving objects.
TuAT2 Topkapı Hall B
Clustering Regular Session
Session chair: Tasdizen, Tolga (Univ. of Utah)
09:00-09:20, Paper TuAT2.1
On Dynamic Weighting of Data in Clustering with K-Alpha Means
Chen, Si-Bao, Anhui Univ.
Wang, Hai-Xian, Southeast Univ.
Luo, Bin, Anhui Univ.
Although many methods of refining initialization have appeared, the sensitivity of K-Means to initial centers is still an
obstacle in applications. In this paper, we investigate a new class of clustering algorithm, K-Alpha Means (KAM), which
is insensitive to the initial centers. With K-Harmonic Means as a special case, KAM dynamically weights data points
during iteratively updating centers, which deemphasizes data points that are close to centers while emphasizes data points
that are not close to any centers. Through replacing minimum operator in K-Means by alpha-mean operator, KAM significantly
improves the clustering performances.
09:20-09:40, Paper TuAT2.2
ARImp: A Generalized Adjusted Rand Index for Cluster Ensembles
Zhang, Shaohong, City Univ. of Hong Kong
Wong, Hau-San, City Univ. of Hong Kong
Adjusted Rand Index (ARI) is one of the most popular measure to evaluate the consistency between two partitions of data
sets in the areas of pattern recognition. In this paper, ARI is generalized to a new measure, Adjusted Rand Index between
a similarity matrix and a cluster partition (ARImp), to evaluate the consistency between a set of clustering solutions (or
cluster partitions) and their associated consensus matrix in a cluster ensemble. The generalization property of ARImp from
ARI is proved and its preservation of desirable properties of ARI is illustrated with simulated experiments. Also, we show
with application experiments on several real data sets that ARImp can serve as a filter to identify the less effective cluster
ensemble methods.
09:40-10:00, Paper TuAT2.3
On the Scalability of Evidence Accumulation Clustering
Lourenço, André, Inst. Superior de Engenharia de Lisboa (ISEL), Inst. Superior Técnico (IST), IT
Fred, Ana Luisa Nobre, Inst. Superior Técnico
Jain, Anil, Michigan State Univ.
This work focuses on the scalability of the Evidence Accumulation Clustering (EAC) method. We first address the space
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