Abstract book (pdf) - ICPR 2010

Answering to a query like when a particular document was printed is quite helpful in practice especially forensic purposes.
This study attempts to develop a general framework that makes use of image processing and pattern recognition principles
for ink age determination in printed documents. The approach, at first, computationally extracts a set of suitable color features
and then analyzes them to properly associate them with ink age. Finally, a neural net is designed and trained to determine
ages of unknown samples. The dataset used for the present experiment consists of the cover pages of LIFE
magazines published in between 1930’s and 70’s (five decades). Test results show that a viable framework for involving
machines in assisting human experts for determining age of printed documents.
13:30-16:30, Paper WeBCT9.23
Automatic Detection and Localization of Natural Scene Text in Video
Huang, Xiaodong, Beijing Univ. of Posts and Telecommunications
Ma, Huadong, Beijing Univ. of Posts and Telecommunications
Video scene text contains semantic information and thus can contribute significantly to video indexing and summarization.
However, most of the previous approaches to detecting scene text from videos experience difficulties in handling texts
with various character size and text alignments. In this paper, we propose a novel algorithm of scene text detection and
localization in video. Based on our observation that text character strokes show intensive edge details in the fixed orientation
no matter what text alignment and size are, a stroke map is first generated. In the scene text detection, we extract
the texture feature of stroke map to locate text lines. The detected scene text lines are accurately located by using Harris
corners in the stroke map. Experimental results show that this approach is robust and can be effectively applied to scene
text detection and localization in video.
13:30-16:30, Paper WeBCT9.24
High-Level Feature Extraction using SIFT GMMs and Audio Models
Inoue, Nakamasa, Tokyo Inst. of Tech.
Saito, Tatsuhiko, Tokyo Inst. of Tech.
Shinoda, Koichi, Tokyo Inst. of Tech.
Furui, Sadaoki,
We propose a statistical framework for high-level feature extraction that uses SIFT Gaussian mixture models (GMMs)
and audio models. SIFT features were extracted from all the image frames and modeled by a GMM. In addition, we used
mel-frequency cepstral coefficients and ergodic hidden Markov models to detect high-level features in audio streams. The
best result obtained by using SIFT GMMs in terms of mean average precision on the TRECVID 2009 corpus was 0.150
and was improved to 0.164 by using audio information.
13:30-16:30, Paper WeBCT9.25
Pairwise Features for Human Action Recognition
Ta, Anh Phuong, Univ. de Lyon, CNRS, INSA-Lyon, LIRIS
Wolf, Christian, INSA de Lyon
Lavoue, Guillaume, Univ. de Lyon, CNRS
Baskurt, Atilla, LIRIS, INSA Lyon
Jolion, Jolion, Univ. de Lyon
Existing action recognition approaches mainly rely on the discriminative power of individual local descriptors extracted
from spatio-temporal interest points (STIP), while the geometric relationships among the local features are ignored. This
paper presents new features, called pairwise features (PWF), which encode both the appearance and the spatio-temporal
relations of the local features for action recognition. First STIPs are extracted, then PWFs are constructed by grouping
pairs of STIPs which are both close in space and close in time. We propose a combination of two codebooks for video
representation. Experiments on two standard human action datasets: the KTH dataset and the Weizmann dataset show that
the proposed approach outperforms most existing methods.
13:30-16:30, Paper WeBCT9.26
Group Activity Recognition by Gaussian Processes Estimation
Cheng, Zhongwei, Chinese Acad. of Sciences
Qin, Lei, Chinese Acad. of Sciences
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