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Hashing methods have proven to be useful for a variety of tasks and have attracted extensive attention in recent years. Various hashing approaches have been proposed to capture similarities between textual, visual, and cross-media information. However, most of the existing works use a bag-of-words methods to represent textual information. Since words with different forms may have similar meaning,...
Various active learning methods have been proposed for image classification problems, while very little work addresses object detection. Measuring the informativeness of an image based on its object windows is a key problem in active learning for object detection. In this paper, an image selection method to select the most representative images is proposed based on measuring their object window distributions...
Object detection is an important and challenging problem in the field of computer vision. Classical object detection approaches such as background subtraction and saliency detection do not require manual collection of training samples, but can be easily affected by noise factors, such as luminance changes and cluttered background. On the other hand, supervised learning based approaches such as Boosting...
Multiple-Instance learning (MIL), which relaxes training annotation granularity from instance level to instance collection (bag) level by applying bag concept, obtains increasing attentions from computer vision community. Due to its flexible annotation mechanism, MIL has been naturally utilized on a variety of computer vision problems. And numerous models have been proposed, each of which is ingeniously...
This paper proposes a novel computational framework for saliency detection, which integrates the saliency map computation and proto-objects detection. The proto-objects are detected based on the saliency map using latent topic model. The detected proto-objects are then utilized to improve the saliency map computation. Extensive experiments are performed on two publicly available datasets. The experimental...
Aiming at the stable walking control problem in the dynamic environments for biped robots, this paper puts forward a method of gait control based on support vector machine(SVM), which provides a solution for the learning control issue based on small sample sizes. Using ankle trajectory and hip trajectory as inputs, and the corresponding trunk trajectory, which guarantees the ZMP criterion as outputs,...
Fuzzy Fisher Criterion(FFC) based clustering method uses the fuzzy Fisher's linear discriminant(FLD) as its clustering objective function and is more robust to noises and outliers than fuzzy c-means clustering(FCM). But FFC can only be used in linear separable dataset. In this paper, a novel fuzzy clustering algorithm, called Kernelized Fuzzy Fisher Criterion(KFFC) based clustering algorithm, is proposed...
In this paper, we propose a new kernel function that makes use of Riemannian geodesic distance s among data points, and present a Geometric median shift algorithm over Riemannian Manifolds. Relying on the geometric median shift, together with geodesic distances, our approach is able to effectively cluster data points distributed on Riemannian manifolds. In addition to improving the clustering results,...
In this paper, we propose a novel multi-class graph boosting algorithm to recognize different visual objects. The proposed method treats subgraph as feature to construct base classifier, and utilizes popular error correcting output code scheme to solve multi-class problem. Both factors, base classifier and error-correcting coding matrix are considered simultaneously. And subgragphs, which are shareable...
In this paper, we propose a high-order graph matching formulation to address non-rigid surface matching. The singleton terms capture the geometric and appearance similarities (e.g., curvature and texture) while the high-order terms model the intrinsic embedding energy. The novelty of this paper includes: 1. casting 3D surface registration into a graph matching problem that combines both geometric...
Multi-instance learning (MIL) has many applications, including image and text categorization. One of the most effective approaches to MIL is by using support vector machines with multi-instance kernels. In this paper we propose a multi-instance kernel, called MIR-kernel, that takes into account the relational information of instances when computing similarities between bags. The relational information...
Information on the vehicular traffic density in an intelligent transport system (ITS) is presently obtained mainly through loop detectors (LD), traffic radars and surveillance cameras. However, the difficulties and cost of installing loop detectors and traffic radars tend to be significant. Currently, a more advanced method of circumventing this is to develop a sort of virtual loop detector (VLD)...
Learning-based approaches for human action recognition often rely on large training sets. Most of these approaches do not perform well when only a few training samples are available. In this paper, we consider the problem of human action recognition from a single clip per action. Each clip contains at most 25 frames. Using a patch based motion descriptor and matching scheme, we can achieve promising...
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