Abstract book (pdf) - ICPR 2010

10:00-10:20, Paper TuAT4.4
Single Channel Speech Separation using Source-Filter Representation
Stark, Michael, Graz Univ. of Tech.
Wohlmayr, Michael, Graz Univ. of Tech.
Pernkopf, Franz, Graz Univ. of Tech.
We propose a fully probabilistic model for source-filter based single channel source separation. In particular, we perform
separation in a sequential manner, where we estimate the source-driven aspects by a factorial HMM used for multi-pitch
estimation. Afterwards, these pitch tracks are combined with the vocal tract filter model to form an utterance dependent
model. Additionally, we introduce a gain estimation approach to enable adaptation to arbitrary mixing levels in the speech
mixtures. We thoroughly evaluate this system and finally end up in a speaker independent model.
10:20-10:40, Paper TuAT4.5
Nonlinear Blind Source Separation using Slow Feature Analysis with Random Features
Ma, Kuijun, Chinese Acad. of Sciences
Tao, Qing, Chinese Acad. of Sciences
Wang, Jue, Chinese Acad. of Sciences
We develop an algorithm RSFA to perform nonlinear blind source separation with temporal constraints. The algorithm is
based on slow feature analysis using random Fourier features for shift invariant kernels, followed by a selection procedure
to obtain the sought-after signals. This method not only obtains remarkable results in a short computing time, but also excellently
handles situations where there are multiple types of mixtures. In kernel methods, since the problem is unsupervised,
the need of multiple kernels is ubiquitous. Experiments on music excerpts illustrate the strong performance of our
method.
TuAT5 Anadolu Auditorium
Image Analysis – III Regular Session
Session chair: Kittler, Josef (Univ. of Surrey)
09:00-09:20, Paper TuAT5.1
Canonical Image Selection by Visual Context Learning
Zhou, Wengang, Univ. of Science and Tech. of China
Lu, Yijuan, Texas State Univ. at San Marcos
Li, Houqiang, Univ. of Science and Tech. of China
Tian, Qi, Univ. of Texas at San Antonio
Canonical image selection is to select a subset of photos that best summarize a photo collection. In this paper, we define
the canonical image as those that contain most important and distinctive visual words. We propose to use visual context
learning to discover visual word significance and develop Weighted Set Coverage algorithm to select canonical images
containing distinctive visual words. Experiments with web image datasets demonstrate that the canonical images selected
by our approach are not only representatives of the collected photos, but also exhibit a diverse set of views with minimal
redundancy.
09:20-09:40, Paper TuAT5.2
Exposing Digital Image Forgeries by using Canonical Correlation Analysis
Zhang, Chi, Beijing Univ. of Tech.
Zhang, Hongbin, Beijing Univ. of Tech.
In this paper, we propose a new method to detect the forgeries in digital images by using photo-response non-uniformity
(PRNU) noise features. The method utilizes canonical correlation analysis (CCA) to measure linear correlation relationship
between two sets of PRNU noise estimation from images taken by the same camera. The linear correlation relationship
maximizes the correlation between the noise reference pattern(or PRNU noise estimation) and PRNU noise features from
the same camera. To further improve the detection accuracy rate, the difference of variance between an image region and
its smoothed version is used to categorize the image region into heavily textured region class or non-heavily textured
region class. For a heavily textured region or a non-heavily textured region, Neyman-Pearson decision is used to calculate
the corresponding threshold, and get the final result of detection.
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