In this paper we propose a new framework for age classification based on human gait using Hidden Markov Model (HMM). A gait database including young people and elderly people is built. To extract appropriate gait feat...
详细信息
Object recognition is challenging problem in computer vision due to appearance variation and presence of visual clutter and occlusions. Recently manifolds are thought to be fundamental for visual perception, and manif...
详细信息
We have recently introduced an incremental learning algorithm, Learn ++ .NSE, designed to learn in nonstationary environments, and has been shown to provide an attractive solution to a number of concept drift problems...
详细信息
We have recently introduced an incremental learning algorithm, Learn ++ .NSE, designed to learn in nonstationary environments, and has been shown to provide an attractive solution to a number of concept drift problems under different drift scenarios. However, Learn ++ .NSE relies on error to weigh the classifiers in the ensemble on the most recent data. For balanced class distributions, this approach works very well, but when faced with imbalanced data, error is no longer an acceptable measure of performance. On the other hand, the well-established SMOTE algorithm can address the class imbalance issue, however, it cannot learn in nonstationary environments. While there is some literature available for learning in nonstationary environments and imbalanced data separately, the combined problem of learning from imbalanced data coming from nonstationary environments is underexplored. Therefore, in this work we propose two modified frameworks for an algorithm that can be used to incrementally learn from imbalanced data coming from a nonstationary environment.
In this paper, we propose spatial localization of multiple sound sources using a spherical robot head equipped with four microphones. We obtain arrival time differences using phase difference candidates. Based on the ...
详细信息
ISBN:
(纸本)9781424493197
In this paper, we propose spatial localization of multiple sound sources using a spherical robot head equipped with four microphones. We obtain arrival time differences using phase difference candidates. Based on the model of precedence effect, arrival temporal disparities obtained from the zero-crossing point are used to calculate time differences and suppress the influence of echoes in a reverberant environment. To integrate spatial cues of different microphone pairs, we use a mapping method from the correlation between different microphone pairs to a 3D map corresponding to azimuth and elevation of sound sources directions. Experiments indicate that the system provides the distribution of sound sources in azimuth-elevation localization, with the EA model even concurrently in reverberant environments.
When using deformable models for the segmentation of biological data, the choice of the best weighting parameters for the internal and external forces is crucial. Especially when dealing with 3D fluorescence microscop...
详细信息
In order to improve the classifier performance in semantic image annotation, we propose a novel method which adopts learning vector quantization (LVQ) technique to optimize low level feature data extracted from given ...
详细信息
In order to improve the classifier performance in semantic image annotation, we propose a novel method which adopts learning vector quantization (LVQ) technique to optimize low level feature data extracted from given image. Some representative vectors are selected with LVQ to train support vector machine (SVM) classifier instead of using all feature data. Performance is compared between the methods with and without feature data optimization when SVM is applied to semantic image annotation. Experiment results show that the proposed method has a better performance than that without using LVQ technique.
In automatic image annotation, it is often extracting low-level visual features from original image for the purpose of mapping to high level image semantic information. In this paper, we propose a novel method which i...
详细信息
In automatic image annotation, it is often extracting low-level visual features from original image for the purpose of mapping to high level image semantic information. In this paper, we propose a novel method which integrates kernel independent component analysis (KICA) and support vector machine (SVM) for analyzing the semantic information of natural images. KICA, which contains a nonlinear kernel mapping component, is adopted to extract low-level features from the original image data. Then these feature vectors are mapped to high-level semantic words using SVM to annotate images with labels in a given semantic label set. Comparative studies have done for the performance of KICA with traditional color histogram and discrete cosine transform features. The experimental results show that the proposed method is capable of extracting the components of images as key features, and with these features to map into semantic categories, higher accuracy is achieved.
In classification of multi-source remote sensing image, it is usually difficult to obtain higher classification accuracy. In the previous work, the modeling technique for the remote sensing image classification based ...
In classification of multi-source remote sensing image, it is usually difficult to obtain higher classification accuracy. In the previous work, the modeling technique for the remote sensing image classification based on the minimum description length (MDL) principle with mixture model is analyzed theoretically. In this work, experimental studies are performed for investigating the modeling technique. With intensive experiments and sophisticated analysis, it is found that the developed modeling technique can build a robust classification system, which can avoid classifier over-fitting training data and make the learning process trade-off between bias and variance. Meanwhile, designed mixture model is more efficient to represent real multi-source remote sensing images compared to single model.
This paper proposes a graph-based method for segmentation of a text image using a selected colour-channel image. The text colour information usually presents a two polarity trend. According to the observation that the...
详细信息
This paper proposes a graph-based method for segmentation of a text image using a selected colour-channel image. The text colour information usually presents a two polarity trend. According to the observation that the histogram distributions of the respective colour channel images are usually different from each other, we select the colour channel image with the histogram having the biggest distance between the two main peaks, which represents the main foreground colour strength and background colour strength respectively. The peak distance is estimated by the mean-shift procedure performed on each individual channel image. Then, a graph model is constructed on a selected channel image to segment the text image into foreground and background. The proposed method is tested on a public database, and its effectiveness is demonstrated by the experimental results.
Gait recognition is a new biometric identification technology. Its aim is to recognize people and detect physiological, pathological and mental characters by their walk style. The feature extraction of gait is the key...
详细信息
Gait recognition is a new biometric identification technology. Its aim is to recognize people and detect physiological, pathological and mental characters by their walk style. The feature extraction of gait is the key step in gait recognition. This paper combines the background subtraction method with symmetric differential method to segment the motion human image, and then extracts the contour of motion human with improved GVF (gradient vector flow) Snake model. The experimental results show that the proposed method can extract contour features effectively for the gait recognition.
暂无评论