Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper ThBCT8.59
Discriminating Intended Human Objects in Consumer Videos
Uegaki, Hiroshi, Osaka Univ.
Nakashima, Yuta, Osaka Univ.
Babaguchi, Noboru, Osaka Univ.
In a consumer video, there are not only intended objects, which are intentionally captured by the camcorder user, but also
unintended objects, which are accidentally framed-in. Since the intended objects are essential to present what the camcorder
user wants to express in the video, discriminating the intended objects from the unintended objects are beneficial for many
applications, e.g., video summarization, privacy protection, and so forth. In this paper, focusing on human objects, we
propose a method for discriminating the intended human objects from the unintended human objects. We evaluated the
proposed method using 10 videos captured by 3 camcorder users. The results demonstrate that the proposed method successfully
discriminates the intended human objects with 0.45 of recall and 0.80 of precision.
13:30-16:30, Paper ThBCT8.60
Detecting Human Activity Profiles with Dirichlet Enhanced Inhomogeneous Poisson Processes
Shimosaka, Masamichi, The Univ. of Tokyo
Ishino, Takahito, The Univ. of Tokyo
Noguchi, Hiroshi, The Univ. of Tokyo
Mori, Taketoshi, The Univ. of Tokyo
Sato, Tomomasa, The Univ. of Tokyo
This paper describes an activity pattern mining method via inhomogeneous Poisson point processes (IPPPs) from timeseries
of count data generated in behavior detection by pyroelectric sensors. IPPP reflects the idea that typical human activity
is rhythmic and periodic. We also focus on the idea that activity patterns are affected by exogenous phenomena,
such as the day of the week, and weather condition. Because single IPPP could not tackle this idea, Dirichlet process mixtures
(DPM) are leveraged in order to discriminate and discover different activity patterns caused by such factors. The use
of DPM leads us to discover the appropriate number of the typical daily patterns automatically. Experimental result using
long-term count data shows that our model successfully and efficiently discovers typical daily patterns.
13:30-16:30, Paper ThBCT8.61
I-FAC: Efficient Fuzzy Associative Classifier for Object Classes in Images
Mangalampalli, Ashish, International Inst. of Information Tech. Hyderabad, India
Chaoji, Vineet, Yahoo! Inc
Sanyal, Subhajit, Yahoo! Lab. Bangalore, India
We present I-FAC, a novel fuzzy associative classification algorithm for object class detection in images using interest
points. In object class detection, the negative class CN is generally vague (CN = U CP ; where U and CP are the universal
and positive classes respectively). But, image classification necessarily requires both positive and negative classes for
training. I-FAC is a single class image classifier that relies only on the positive class for training. Because of its fuzzy
nature, I-FAC also handles polysemy and synonymy (common problems in most crisp (non-fuzzy) image classifiers) very
well. As associative classification leverages frequent patterns mined from a given dataset, its performance as adjudged
from its false-positive-rate(FPR)-versus-recall curve is very good, especially at lower FPRs when its recall is even better.
IFAC has the added advantage that the rules used for classification have clear semantics, and can be comprehended easily,
unlike other classifiers, such as SVM, which act as black-boxes. From an empirical perspective (on standard public
datasets), the performance of I-FAC is much better, especially at lower FPRs, than that of either bag-of-words (BOW) or
SVM (both using interest points).
13:30-16:30, Paper ThBCT8.62
Audio-Visual Data Fusion using a Particle Filter in the Application of Face Recognition
Steer, Michael, Otto-von-guericke-Univ. Magdeburg
This paper describes a methodology by which audio and visual data about a scene can be fused in a meaningful manner
in order to locate a speaker in a scene. This fusion is implemented within a Particle Filter such that a single speaker can
be identified in the presence of multiple visual observations. The advantages of this fusion are that weak sensory data
from either modality can be reinforced and the presence of noise can be reduced.
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