Abstract book (pdf) - ICPR 2010

TuAT1 Marmara Hall
Object Detection and Recognition – I Regular Session
Session chair: Jiang, Xiaoyi (Univ. of Münster)
09:00-09:20, Paper TuAT1.1
Learning an Efficient and Robust Graph Matching Procedure for Specific Object Recognition
Revaud, Jerome, Univ. de Lyon, CNRS
Lavoue, Guillaume, Univ. de Lyon, CNRS
Ariki, Yasuo, Kobe Univ.
Baskurt, Atilla, LIRIS, INSA Lyon
We present a fast and robust graph matching approach for 2D specific object recognition in images. From a small number
of training images, a model graph of the object to learn is automatically built. It contains its local key points as well as
their spatial proximity relationships. Training is based on a selection of the most efficient subgraphs using the mutual information.
The detection uses dynamic programming with a lattice and thus is very fast. Experiments demonstrate that
the proposed method outperforms the specific object detectors of the state-of-the-art in realistic noise conditions.
09:20-09:40, Paper TuAT1.2
A New Biologically Inspired Feature for Scene Image Classification
Jiang, Aiwen, Chinese Acad. of Sciences
Wang, Chunheng, Chinese Acad. of Sciences
Xiao, Baihua, Chinese Acad. of Sciences
Dai, Ruvei, Chinese Acad. of Sciences
Scene classification is a hot topic in pattern recognition and computer vision area. In this paper, based on the past research
on vision neuroscience, we proposed a new biologically inspired feature method for scene image classification. The new
feature accounts for the visual processing from simple cell to complex cell in V1 area, and also the spatial layout for scene
gist signature. It provides a different line and model revision to consider some nonlinearities inV1 area. We compare it
with traditional HMAX model and recently proposed ScSPM model, and experiment on a popular 15 scenes dataset. We
show that our proposed method has many important differences and merits. The experiment results also show that our
method outperforms the state-of-the-art like ScSPM and KSPM model.
09:40-10:00, Paper TuAT1.3
On a Quest for Image Descriptors based on Unsupervised Segmentation Maps
Koniusz, Piotr, Univ. of Surrey
Mikolajczyk, Krystian, Univ. of Surrey
This paper investigates segmentation-based image descriptors for object category recognition. In contrast to commonly
used interest points the proposed descriptors are extracted from pairs of adjacent regions given by a segmentation method.
In this way we exploit semi-local structural information from the image. We propose to use the segments as spatial bins
for descriptors of various image statistics based on gradient, colour and region shape. Proposed descriptors are validated
on standard recognition benchmarks. Results show they outperform state-of-the-art reference descriptors with 5.6x less
data and achieve comparable results to them with 8.6x less data. The proposed descriptors are complementary to SIFT
and achieve state-of-the-art results when combined together within a kernel based classifier.
10:00-10:20, Paper TuAT1.4
An RST-Tolerant Shape Descriptor for Object Detection
Su, Chih-Wen, Acad. Sinica
Liao, Mark, Acad. Sinica, Taiwan
Liang, Yu-Ming, Acad. Sinica
Tyan, Hsiao-Rong, Chung Yuan Christian Univ.
In this paper, we propose a new object detection method that does not need a learning mechanism. Given a hand-drawn
model as a query, we can detect and locate objects that are similar to the query model in cluttered images. To ensure the
invariance with respect to rotation, scaling, and translation (RST), high curvature points (HCPs) on edges are detected
first. Each pair of HCPs is then used to determine a circular region and all edge pixels covered by the circular region are
- 71 -