Abstract book (pdf) - ICPR 2010

image that it is currently displayed and data of the rest of the temporally adjacent images. This scheduler uses a model based
on the quality progression of the image in order to estimate which percentage of the bandwidth is dedicated to prefetch data.
Our experimental results prove that a significant benefit can be achieved in terms of both subjective quality and responsiveness
by means of prefetching.
13:30-16:30, Paper WeBCT9.15
Binarization of Color Characters in Scene Images using K-Means Clustering and Support Vector Machines
Wakahara, Toru, Hosei Univ.
Kita, Kohei, Hosei Univ.
This paper proposes a new technique for binalizing multicolored characters subject to heavy degradations. The key ideas are
threefold. The first is generation of tentatively binarized images via every dichotomization of k clusters obtained by k-means
clustering in the HSI color space. The total number of tentatively binarized images equals 2^k2. The second is use of support
vector machines (SVM) to determine whether and to what degree each tentatively binarized image represents a character or
non-character. We feed the SVM with mesh and weighted direction code histogram features to output the degree of character-likeness.
The third is selection of a single binarized image with the maximum degree of character likeness as an optimal
binarization result. Experiments using a total of 1000 single-character color images extracted from the ICDAR 2003 robust
OCR dataset show that the proposed method achieves a correct binarization rate of 93.7%.
13:30-16:30, Paper WeBCT9.16
A Self-Training Learning Document Binarization Framework
Su, Bolan, National Univ. of Singapore
Lu, Shijian, -
Tan, Chew-Lim, National Univ. of Singapore
Document Image Binarization techniques have been studied for many years, and many practical binarization techniques have
been developed and applied successfully on commercial document analysis systems. However, the current state-of-the-art
methods, fail to produce good binarization results for many badly degraded document images. In this paper, we propose a
self-training learning framework for document image binarization. Based on reported binarization methods, the proposed
framework first divides document image pixels into three categories, namely, foreground pixels, background pixels and uncertain
pixels. A classifier is then trained by learning from the document image pixels in the foreground and background categories.
Finally, the uncertain pixels are classified using the learned pixel classifier. Extensive experiments have been
conducted over the dataset that is used in the recent Document Image Binarization Contest(DIBCO) 2009. Experimental results
show that our proposed framework significantly improves the performance of reported document image binarization
methods.
13:30-16:30, Paper WeBCT9.17
Novel Edge Features for Text Frame Classification in Video
Palaiahnakote, Shivakumara, National Univ. of Singapore
Tan, Chew-Lim, National Univ. of Singapore
Text frame classification is needed in many applications such as event identification, exact event boundary identification,
navigation, video surveillance in multimedia etc. To the best of our knowledge, there are no methods reported solely dedicated
to text frame classifications so far. Hence this paper presents a new approach to text frame classification in video based on
capturing local observable edge properties of text frames, by virtue of the strong presence of sharp edges, straight appearances
of edges and consistent proximity between edges. The approach initially classifies the blocks of the frame into text blocks
and non-text blocks. The true text block is then identified among classified text blocks to detect text frames by the proposed
features. If the text frame produces one true text block then it is considered as a text frame otherwise a non-text frame. We
evaluate the proposed approach on a large database containing both text and nontext frames and publicly available data at
two levels, i.e., estimating recall and precision at the block level and the frame level.
13:30-16:30, Paper WeBCT9.18
Image Matching and Retrieval by Repetitive Patterns
Doubek, Petr, Czech Tech. Univ. in Prague
Matas, Jiri, Czech Tech. Univ. in Prague
- 231 -