Abstract book (pdf) - ICPR 2010

similarities among face carvings are computed by performing Procrustes analysis on local facial features (eyes, nose,
mouth, etc.). The distance between corresponding face features is computed using point distribution models; the final pairwise
similarity is the weighted sum of feature similarities. A web-based interface is provided to allow domain experts to
interactively assign different weights to each face feature, and display hierarchical clustering results in 2D or 3D projections
obtained by multidimensional scaling. The proposed framework has been successfully applied to the devata goddesses
depicted in the ancient Angkor Wat temple. The resulting clusterings and visualization will enable a systematic anthropological,
ethnological and artistic analysis of nearly 1,800 stone portraits of devatas of Angkor Wat.
TuCT2 Topkapı Hall A
Feature Extraction – II Regular Session
Session chair: Covell, Michele (Google, Inc.)
15:40-16:00, Paper TuCT2.1
Action Recognition using Spatial-Temporal Context
Hu, Qiong, Chinese Acad. of Sciences
Qin, Lei, Chinese Acad. of Sciences
Huang, Qingming, Chinese Acad. of Sciences
Jiang, Shuqiang, Chinese Acad. of Sciences
Tian, Qi, Univ. of Texas at San Antonio
The spatial-temporal local features and the bag of words representation have been widely used in the action recognition
field. However, this framework usually neglects the internal spatial-temporal relations between video-words, resulting in
ambiguity in action recognition task, especially for videos in the wild. In this paper, we solve this problem by utilizing the
volumetric context around a video-word. Here, a local histogram of video-words distribution is calculated, which is referred
as the context and further clustered into contextual words. To effectively use the contextual information, the descriptive
video-phrases (ST-DVPs) and the descriptive video-cliques (ST-DVCs) are proposed. A general framework for ST-DVP
and ST-DVC generation is described, and then action recognition can be done based on all these representations and their
combinations. The proposed method is evaluated on two challenging human action datasets: the KTH dataset and the
YouTube dataset. Experiment results confirm the validity of our approach.
16:00-16:20, Paper TuCT2.2
Feature Extraction for Simple Classification
Stuhlsatz, André, Univ. of Applied Sciences Duesseldorf
Lippel, Jens, Univ. of Applied Sciences Duesseldorf
Zielke, Thomas, Univ. of Applied Sciences Duesseldorf
Constructing a recognition system based on raw measurements for different objects usually requires expert knowledge of
domain specific data preprocessing, feature extraction, and classifier design. We seek to simplify this process in a way
that can be applied without any knowledge about the data domain and the specific properties of different classification algorithms.
That is, a recognition system should be simple to construct and simple to operate in practical applications. For
this, we have developed a nonlinear feature extractor for high-dimensional complex patterns, using Deep Neural Networks
(DNN). Trained partly supervised and unsupervised, the DNN effectively implements a nonlinear discriminant analysis
based on a Fisher criterion in a feature space of very low dimensions. Our experiments show that the automatically extracted
features work very well with simple linear discriminants, while the recognition rates improve only minimally if more sophisticated
classification algorithms like Support Vector Machines (SVM) are used instead.
16:20-16:40, Paper TuCT2.3
Towards a Generic Feature-Selection Measure for Intrusion Detection
Nguyen, Hai Thanh, Gjøvik Univ. Coll.
Franke, Katrin, Gjøvik Univ. Coll.
Petrovic, Slobodan, Gjøvik Univ. Coll.
Performance of a pattern recognition system depends strongly on the employed feature-selection method. We perform an
in-depth analysis of two main measures used in the filter model: the correlation-feature-selection (CFS) measure and the
minimal-redundancy-maximal-relevance (mRMR) measure. We show that these measures can be fused and generalized
into a generic feature-selection (GeFS) measure. Further on, we propose a new feature-selection method that ensures glob-
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