Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper WeBCT8.40
Image Categorization by Learned Nonlinear Subspace of Combined Visual-Words and Low-Level Features
Han, Xian-Hua, Ritsumeikan Univ.
Chen, Yen-Wei, Ritsumeikan Univ.
Ruan, Xiang, Omron Corparation
Image category recognition is important to access visual information on the level of objects and scene types. This paper
presents a new algorithm for the automatic recognition of object and scene classes. Compact and yet discriminative visual-words
and low-level-features object class subspaces are automatically learned from a set of training images by a Supervised
Nonlinear Neighborhood Embedding (SNNE) algorithm, which can learn an adaptive nonlinear subspace by
preserving the neighborhood structure of the visual feature space. The main contribution of this paper is two fold: i) an
optimally compact and discriminative feature subspace is learned by the proposed SNNE algorithm for different feature
space (visual-word and low-level features). ii) An effective merge of different feature subspace can be implemented simply.
High classification accuracy is demonstrated on different database including the scene databas (Simplicity) and object
recognition database (Caltech). We confirm that the proposed strategy is much better than state-of-the-art methods for different
databases.
13:30-16:30, Paper WeBCT8.41
Can Motion Segmentation Improve Patch-Based Object Recognition?
Ulges, Adrian, DFKI
Breuel, Thomas -
Patch-based methods, which constitute the state of the art in object recognition, are often applied to video data, where
motion information provides a valuable clue for separating objects of interest from the background. We show that such
motion-based segmentation improves the robustness of patch-based recognition with respect to clutter. Our approach,
which employs segmentation information to rule out incorrect correspondences between training and test views, is demonstrated
empirically to distinctly outperform baselines operating on unsegmented images. Relative improvements reach
50% for the recognition of specific objects, and 33% for object category retrieval.
13:30-16:30, Paper WeBCT8.42
Semi-Supervised and Interactive Semantic Concept Learning for Scene Recognition
Han, Xian-Hua, Ritsumeikan Univ.
Chen, Yen-Wei, Ritsumeikan Univ.
Ruan, Xiang, Omron Corparation
In this paper, we present a novel semi-supervised and interactive concept learning algorithm for scene recognition by local
semantic description. Our work is motivated by the continuing effort in content-based image retrieval to extract and to
model the semantic content of images. The basic idea of the semantic modeling is to classify local image regions into semantic
concept classes such as water, sunset, or sky [1]. However, labeling concept sampling manually for training semantic
model is fairly expensive, and the labeling results is, to some extent, subjective to the operators. In this paper, by using
the proposed semi-supervised and interactive learning algorithm, training samples and new concepts can be obtained accurately
and efficiently. Through extensive experiments, we demonstrate that the image concept representation is well
suited for modeling the semantic content of heterogenous scene categories, and thus for recognition and retrieval. Furthermore,
higher recognition accuracy can be achieved by updating new training samples and concepts, which are obtained
by the novel proposed algorithm.
13:30-16:30, Paper WeBCT8.43
Dense Structure Inference for Object Classification in Aerial LIDAR Dataset
Kim, Eunyoung, Univ. of Southern California
Medioni, Gerard, Univ. of Southern California
We present a framework to classify small freeform objects in 3D aerial scans of a large urban area. The system first identifies
large structures such as the ground surface and roofs of buildings densely built in the scene, by fitting planar patches
and grouping adjacent patches similar in pose together. Then, it segments initial object candidates which represent the
visible surface of an object using the identified structures. To deal with sparse density in points representing each candidate,
we also propose a novel method to infer a dense 3D structure from the given sparse and noisy points without any meshes
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