Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper WeBCT9.34
Comparison of Multidimensional Data Access Methods for Feature-Based Image Retrieval
Arslan, Serdar, Middle East Tech. Univ.
Açar, Esra, Middle East Tech. Univ.
Saçan, Ahmet, Middle East Tech. Univ.
Toroslu, Ismail Hakkı , Middle East Tech. Univ.
Yazıcı, Adnan, Middle East Tech. Univ.
Within the scope of information retrieval, efficient similarity search in large document or multimedia collections is a
critical task. In this paper, we present a rigorous comparison of three different approaches to the image retrieval problem,
including cluster-based indexing, distance-based indexing, and multidimensional scaling methods. The time and accuracy
trade-offs for each of these methods are demonstrated on a large Corel image database. Similarity of images is obtained
via a feature-based similarity measure using four MPEG-7 low-level descriptors. We show that an optimization of feature
contributions to the distance measure can identify irrelevant features and is necessary to obtain the maximum accuracy.
We further show that using multidimensional scaling can achieve comparable accuracy, while speeding-up the query times
significantly by allowing the use of spatial access methods.
13:30-16:30, Paper WeBCT9.35
A Pixel-Based Evaluation Method for Text Detection in Color Images
Anthimopoulos, Marios, National Center for Scientific Res. “Demokritos”
Vlissidis, Nikolaos, National Center for Scientific Res. “Demokritos”
Gatos, B., National Center for Scientific Res. “Demokritos”
This paper proposes a performance evaluation method for text detection in color images. The method, contrary to previous
approaches, is not based on the inexplicitly defined text bounding boxes for the evaluation of the text detection result but
considers only the text pixels detected by binarizing the image and applying a color inversion if needed. Moreover, in
order to gain independence from the chosen binarization algorithm, the method uses the skeleton of the binarized image.
The results produced by the proposed evaluation protocol proved to be quite representative and reasonable compared to
the corresponding optical result.
13:30-16:30, Paper WeBCT9.36
Active Boosting for Interactive Object Retrieval
Lechervy, Alexis, ETIS, CNRS, ENSEA, Univ. Cergy-Pontoise
Gosselin, Philippe Henri, CNRS
Precioso, Frederic, ETIS, CNRS, ENSEA, Univ. Cergy-Pontoise
This paper presents a new algorithm based on boosting for interactive object retrieval in images. Recent works propose
online boosting algorithms where weak classifier sets are iteratively trained from data. These algorithms are proposed for
visual tracking in videos, and are not well adapted to online boosting for interactive retrieval. We propose in this paper to
iteratively build weak classifiers from images, labeled as positive by the user during a retrieval session. A novel active
learning strategy for the selection of images for user annotation is also proposed. This strategy is used to enhance the
strong classifier resulting from boosting process, but also to build new weak classifiers. Experiments have been carried
out on a generalist database in order to compare the proposed method to a SVM based reference approach.
13:30-16:30, Paper WeBCT9.37
Geotagged Photo Recognition using Corresponding Aerial Photos with Multiple Kernel Learning
Keita, Yaegashi, Univ. of Electro-Commnications
Keiji, Yanai, Univ. of Electro-Commnications
In this paper, we treat with generic object recognition for geotagged images. As a recognition method for geotagged photos,
we have already proposed exploiting aerial photos around geotag places as additional image features for visual recognition
of geotagged photos. In the previous work, to fuse two kinds of features, we just concatenate them. Instead, in this paper,
we introduce Multiple Kernel Learning (MKL) to integrate both features of photos and aerial images. MKL can estimate
the contribution weights to integrate both kinds of features. In the experiments, we confirmed effectiveness of usage of
aerial photos for recognition of geotagged photos, and we evaluated the weights of both features estimated by MKL for
eighteen concepts.
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