Automatic image segmentation is always a fundamental but challenging problem in computervision. The simplest approach to image segmentation may be clustering feature vectors of pixels at first, then labeling each pix...
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Automatic image segmentation is always a fundamental but challenging problem in computervision. The simplest approach to image segmentation may be clustering feature vectors of pixels at first, then labeling each pixel with its corresponding cluster. This requires that the clustering on feature space must be robust. However, most of popular clustering algorithms could not obtain a robust clustering result yet, if the clusters in feature space have a complex distribution. Generally, for most of clustering-based segmentation methods, it still needs more constraints of positional relations between pixels in image lattice to be utilized during the procedure of clustering. Our works in this paper address the problem of image segmentation under the paradigm of pure clustering-then-labeling. A robust clustering algorithm which could maintain good coherence of data in feature space is proposed and utilized to do clustering on the L*a*b* color feature space of pixels. Image segmentation is straightforwardly obtained by setting each pixel with its corresponding cluster. Further, based on the theory of Minimum Description Length, an effective approach to automatic parameter selection for our segmentation method is also proposed. We test our segmentation method on Berkeley segmentation database, and the experimental results show that our method compares favorably against some state-of-the-art segmentation methods. (C) 2011 Elsevier B.V. All rights reserved.
The dynamics of image acquisition conditions for gastroenterology imaging scenarios pose novel challenges for automatic computer assisted decision systems. Such systems should have the ability to mimic the tissue char...
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The codebook based (bag-of-words) model is a widely applied model for image classification. We analyze recent coding strategies in this model, and find that saliency is the fundamental characteristic of coding. The sa...
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ISBN:
(纸本)9781457703942
The codebook based (bag-of-words) model is a widely applied model for image classification. We analyze recent coding strategies in this model, and find that saliency is the fundamental characteristic of coding. The saliency in coding means that if a visual code is much closer to a descriptor than other codes, it will obtain a very strong response. The salient representation under maximum pooling operation leads to the state-of-the-art performance on many databases and competitions. However, most current coding schemes do not recognize the role of salient representation, so that they may lead to large deviations in representing local descriptors. In this paper, we propose "salient coding", which employs the ratio between descriptors' nearest code and other codes to describe descriptors. This approach can guarantee salient representation without deviations. We study salient coding on two sets of image classification databases (15-Scenes and PASCAL VOC2007). The experimental results demonstrate that our approach outperforms all other coding methods in image classification.
Object localization is a challenging problem due to variations in object's structure and illumination. Although existing part based models have achieved impressive progress in the past several years, their improve...
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ISBN:
(纸本)9781457703942
Object localization is a challenging problem due to variations in object's structure and illumination. Although existing part based models have achieved impressive progress in the past several years, their improvement is still limited by low-level feature representation. Therefore, this paper mainly studies the description of object structure from both feature level and topology level. Following the bottom-up paradigm, we propose a boosted Local Structured HOGLBP based object detector. Firstly, at feature level, we propose Local Structured Descriptor to capture the object's local structure, and develop the descriptors from shape and texture information, respectively. Secondly, at topology level, we present a boosted feature selection and fusion scheme for part based object detector. All experiments are conducted on the challenging PASCAL VOC2007 datasets. Experimental results show that our method achieves the state-of-the-art performance.
The proceedings contain 100 papers. The topics discussed include: human recognition in a video network;the guaranteed cost switch control of BTT vehicle based on RBF-NN compensation;a novel method for evaluating the v...
ISBN:
(纸本)9780819478078
The proceedings contain 100 papers. The topics discussed include: human recognition in a video network;the guaranteed cost switch control of BTT vehicle based on RBF-NN compensation;a novel method for evaluating the validity of the visual attended regions based on SIFT descriptors;human body motion tracking based on quantum-inspired immune cloning algorithm;object detection with geometric context of keypoints described as lifetime;a combined feature latent semantic model for scene classification;spectral clustering with eigenvector selection based on ensemble ranking;to generate a finite element model of human thorax using the VCH dataset;three-dimensional building reconstruction using highly overlapped aerial images;study on human vision model of the multi-parameter correction factor;a hybrid registration approach in super-resolution reconstruction for visual surveillance application;and realistic generation of natural phenomena based on video synthesis.
Object detection is a fundamental task in computervision. Deformable part based model has achieved great success in the past several years, demonstrating very promising performance. Many papers emerge on part based m...
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ISBN:
(纸本)9781457701221
Object detection is a fundamental task in computervision. Deformable part based model has achieved great success in the past several years, demonstrating very promising performance. Many papers emerge on part based model such as structure learning, learning more discriminative features. To help researchers better understand the existing visual features’ potential for part based object detection and promote the deep research into part based object representation, we propose an evaluation framework to compare various visual features’ performance for part based model. The evaluation is conducted on challenging PASCAL VOC2007 dataset which is widely recognized as a benchmark database. We adopt Average Precision (AP) score to measure each detector’s performance. Finally, the full evaluation results are present and discussed.
Gait is a well recognized biometric feature that is used to identify a human at a distance. However, in real environment, appearance changes of individuals due to viewing angle changes cause many difficulties for gait...
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ISBN:
(纸本)9781424469840
Gait is a well recognized biometric feature that is used to identify a human at a distance. However, in real environment, appearance changes of individuals due to viewing angle changes cause many difficulties for gait recognition. This paper re-formulates this problem as a regression problem. A novel solution is proposed to create a View Transformation Model (VTM) from the different point of view using Support Vector Regression (SVR). To facilitate the process of regression, a new method is proposed to seek local Region of Interest (ROI) under one viewing angle for predicting the corresponding motion information under another viewing angle. Thus, the well constructed VTM is able to transfer gait information under one viewing angle into another viewing angle. This proposal can achieve view-independent gait recognition. It normalizes gait features under various viewing angles into a common viewing angle before similarity measurement is carried out. The extensive experimental results based on widely adopted benchmark dataset demonstrate that the proposed algorithm can achieve significantly better performance than the existing methods in literature.
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