Abstract book (pdf) - ICPR 2010

14:50-15:10, Paper TuBT2.5
Compressing Sparse Feature Vectors using Random Ortho-Projections
Rahtu, Esa, Univ. of Oulu
Salo, Mikko, Univ. of Helsinki
Heikkilä, Janne, Univ. of Oulu
In this paper we investigate the usage of random ortho-projections in the compression of sparse feature vectors. The study
is carried out by evaluating the compressed features in classification tasks instead of concentrating on reconstruction accuracy.
In the random ortho-projection method, the mapping for the compression can be obtained without any further
knowledge of the original features. This makes the approach favorable if training data is costly or impossible to obtain.
The independence from the data also enables one to embed the compression scheme directly into the computation of the
original features. Our study is inspired by the results in compressive sensing, which state that up to a certain compression
ratio and with high probability, such projections result in no loss of information. In comparison to learning based compression,
namely principal component analysis (PCA), the random projections resulted in comparable performance already
at high compression ratios depending on the sparsity of the original features.
TuBT3 Marmara Hall
Object Detection and Recognition – II Regular Session
Session chair: Porikli, Fatih (MERL)
13:30-13:50, Paper TuBT3.1
Learning Discriminative Features based on Distribution
Shen, Jifeng, Southeast Univ.
Yang, Wankou, Southeast Univ.
Sun, Changyin, Southeast Univ.
In this paper, a novel feature named adaptive projection LBP (APLBP) is proposed for face detection. To promote discriminative
power, the distribution information of training samples is embedded into the proposed feature. APLBP is generated
by LDA which maximizes the margin between positive and negative samples adaptively, utilizing characteristics
of similarity to Gaussian distribution of the training samples. Asymmetric Gentle Adaboost is utilized to train strong classifier
and nested cascade is applied to construct the final detector. Experimental results based on MIT+CMU database
demonstrate that APLBP feature outperforms several well-existing features due to its excellent discriminative power with
less feature number.
13:50-14:10, Paper TuBT3.2
Sub-Category Optimization for Multi-View Multi-Pose Object Detection
Das, Dipankar, Saitama Univ.
Kobayashi, Yoshinori, Saitama Univ.
Kuno, Yoshinori, Saitama Univ.
Object category detection with large appearance variation is a fundamental problem in computer vision. The appearance
of object categories can change due to intra-class variability, viewpoint, and illumination. For object categories with large
appearance change a sub-categorization based approach is necessary. This paper proposes a sub-category optimization
approach that automatically divides an object category into an appropriate number of sub-categories based on appearance
variation. Instead of using a predefined intra-category sub-categorization based on domain knowledge or validation
datasets, we divide the sample space by unsupervised clustering based on discriminative image features. Then the clustering
performance is verified using a sub-category discriminant analysis. Based on the clustering performance of the unsupervised
approach and sub-category discriminant analysis results we determine an optimal number of sub-categories per object category.
Extensive experimental results are shown using two standard and the authors’ own databases. The comparison
results show that our approach outperforms the state-of-the-art methods.
14:10-14:30, Paper TuBT3.3
Learning and Detection of Object Landmarks in Canonical Object Space
Kamarainen, Joni-Kristian, Lappeenranta Univ. of Tech.
Ilonen, Jarmo, Lappeenranta Univ. of Tech.
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