Abstract book (pdf) - ICPR 2010

09:40-10:00, Paper TuAT5.3
Adding Affine Invariant Geometric Constraint for Partial-Duplicate Image Retrieval
Wu, Zhipeng, Chinese Acad. of Sciences
Xu, Qianqian, Chinese Acad. of Sciences
Jiang, Shuqiang, Chinese Acad. of Sciences
Huang, Qingming, Chinese Acad. of Sciences
Cui, Peng, Chinese Acad. of Sciences
Li, Liang, Chinese Acad. of Sciences
The spring up of large numbers of partial-duplicate images on the internet brings a new challenge to the image retrieval
systems. Rather than taking the image as a whole, researchers bundle the local visual words by MSER detector into groups
and add simple relative ordering geometric constraint to the bundles. Experiments show that bundled features become
much more discriminative than single feature. However, the weak geometric constraint is only applicable when there is
no significant rotation between duplicate images and it couldn’t handle the circumstances of image flip or large rotation
transformation. In this paper, we improve the bundled features with an affine invariant geometric constraint. It employs
area ratio invariance property of affine transformation to build the affine invariant matrix for bundled visual words. Such
affine invariant geometric constraint can cope well with flip, rotation or other transformations. Experimental results on
the internet partial-duplicate image database verify the promotion it brings to the original bundled features approach. Since
currently there is no available public corpus for partial-duplicate image retrieval, we also publish our dataset for future
studies.
10:00-10:20, Paper TuAT5.4
Outlier-Resistant Dissimilarity Measure for Feature-Based Image Matching
Palenichka, Roman, Univ. of Quebec
Lakhssassi, Ahmed, Univ. of Quebec
Zaremba, Marek, Univ. of Quebec
A novel dissimilarity measure is proposed to perform correspondence image matching for object recognition, image registration
and content-based image retrieval. This is a feature-based matching, which supposes image representation (object
description) in the form of a set of multi-location descriptor vectors. The proposed measure called intersection matching
distance eliminates outlies (false or missing feature points) while transformation-invariantly matching two sets of descriptor
vectors. A block-subdivision algorithm for time-efficient image matching is also described.
10:20-10:40, Paper TuAT5.5
The University of Surrey Visual Concept Detection System at ImageCLEF@ICPR: Working Notes
Tahir, Muhammad Atif, Univ. of Surrey
Fei, Yan, Univ. of Surrey
Barnard, Mark, Univ. of Surrey
Awais, Muhammad, Univ. of Surrey
Mikolajczyk, Krystian, Univ. of Surrey
Kittler, Josef, Univ. of Surrey
Visual concept detection is one of the most important tasks in image and video indexing. This paper describes our system
in the Image CLEF@ICPR Visual Concept Detection Task which ranked {\it first} for large-scale visual concept detection
tasks in terms of Equal Error Rate (EER) and Area under Curve (AUC) and ranked {\it third} in terms of hierarchical
measure. The presented approach involves state-of-the-art local descriptor computation, vector quantisation via clustering,
structured scene or object representation via localised histograms of vector codes, similarity measure for kernel construction
and classifier learning. The main novelty is the classifier-level and kernel-level fusion using Kernel Discriminant Analysis
with RBF/Power Chi-Squared kernels obtained from various image descriptors. For 32 out of 53 individual concepts, we
obtain the best performance of all 12 submissions to this task.
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