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Constructing effective representations is a critical but challenging problem in multimedia understanding. The traditional handcraft features often rely on domain knowledge, limiting the performances of exiting methods. This paper discusses a novel computational architecture for general image feature mining, which assembles the primitive filters (i.e. Gabor wavelets) into compositional features in...
In this paper, we introduce a fully autonomous vehicle classification system that continuously learns from largeamounts of unlabeled data. For that purpose, we proposea novel on-line co-training method based on visual and acoustic information. Our system does not need complicated microphone arrays or video calibration and automatically adapts to specific traffic scenes. These specialized detectors...
Previous Multiple Kernel Learning approaches (MKL) employ different kernels by their linear combination. Though some improvements have been achieved over methods using single kernel, the advantages of employing multiple kernels for machine learning are far from being fully developed. In this paper, we propose to use “high order kernels” to enhance the learning of MKL when a set of original kernels...
A recent dominating trend in tracking called tracking-by-detection uses on-line classifiers in order to redetect objects over succeeding frames. Although these methods usually deliver excellent results and run in real-time they also tend to drift in case of wrong updates during the self-learning process. Recent approaches tackled this problem by formulating tracking-by-detection as either one-shot...
In this paper, we propose a novel approach to automatically generating, instead of manually designing, discriminative visual features for face detection. The features are composed by multiple local features (e.g., Haar features), and such features can capture not only the local texture information but also their spatial configurations. Therefore, the proposed feature contains rich semantic information...
In this paper we address the problem of generative object categorization in computer vision. We propose a Bayesian model using hierarchical Dirichlet processes mixing AdaBoost learning. Although previous methods trained HDP model for one or two latent themes, our proposed approach uses small-patch-independent-words of appearance-based descriptor and shape information to train a set of intermediate...
Effects of driver's states adaptive driving support systems is highly expected for the prevention of traffic accidents. In order to create this constituent technology, detecting driver's psychosomatic states which occurs just before a traffic accident is essential. Therefore driver's distraction is thought as one of important factors. This study focused on detecting driver's cognitive distraction,...
Advances in video technology are being incorporated into todaypsilas medical research and education. Medical videos contain important medical events, such as diagnostic or therapeutic operations. Automatic discovery and classification of these events are highly desirable and very useful. In this paper, we present a novel method for multi-class educational medical video event categorization. Our method...
On-line boosting allows to adapt a trained classifier to changing environmental conditions or to use sequentially available training data. Yet, two important problems in the on-line boosting training remain unsolved: (i) classifier evaluation speed optimization and, (ii) automatic classifier complexity estimation. In this paper we show how the on-line boosting can be combined with Waldpsilas sequential...
Automatic image annotation (AIA) refers to the association of words to whole images which is considered as a promising and effective approach to bridge the semantic gap between low-level visual features and high-level semantic concepts. In this paper, we formulate the task of image annotation as a multi-label multi class semantic image classification problem and propose a simple yet effective algorithm:...
Image classification approach is one promising method used for automatic image annotation. In order to improve image annotation accuracy, recent researchers propose to use AdaBoost algorithm for the ensemble of classifiers. But as it is difficult for AdaBoost algorithm to search a large feature space, only fewer features are used for the construction of weak classifiers in ensemble. As a result, it...
The required amount of labeled training data for object detection and classification is a major drawback of current methods. Combining labeled and unlabeled data via semi-supervised learning holds the promise to ease the tedious and time consuming labeling effort. This paper presents a novel semi-supervised learning method which combines the power of learned similarity functions and classifiers. The...
We consider the problem of visual categorization with minimal supervision during training. We propose a partbased model that loosely captures structural information. We represent images as a collection of parts characterized by an appearance codeword from a visual vocabulary and by a neighborhood context, organized in an ordered set of bag-of-features representations. These bags are computed in a...
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