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This paper proposes an approach to extract focused objects (i.e., attention objects) from low depth-of-field images. To recognize the focused object, we decompose the image into multiple regions, which are described by using three types of visual descriptors. Each descriptor is extracted from a representation of some aspects of local appearance, e.g., a spatially localized texture, color, or geometrical...
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...
This work addresses the problem of large-scale visual concept detection. Visual concepts are usually learned from an annotated image or video database with a machine learning algorithm, posing this problem as a multiclass supervised learning task. Some practical issues appear when the number of concept grows, in particular when one aims at developing applications for real users, restricting the constraints...
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...
Training object detectors aims at choosing specific visual attributes which are efficient and optimal for each learned object. This paper presents a new process which achieves this goal by putting all families of descriptors one wants to consider in a pool of descriptors and by letting the algorithm build a cascade with the most efficient descriptors by introducing management of very large features...
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...
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...
In this paper we propose a confidence rated boosting algorithm based on Ada-boost for generic object detection. Confidence rated Ada-boost algorithm has not been applied to generic object detection problem; in that sense our work is novel. We represent images as bag of words, where the words are SIFT descriptors extracted over some interest points. We compare our boosting algorithm to another version...
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:...
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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