Learn More
In multiple instance learning (MIL), how the instances determine the bag-labels is an essential issue, both algorithmically and intrinsically. In this paper, we show that the <i>mechanism</i> of how the instances determine the bag-labels is different for different application domains, and does not necessarily obey the traditional assumptions of MIL. We(More)
This paper presents a simple and highly effective system for robust texture classification, based on (1) random local features, (2) a simple global Bag-of-Words (BoW) representation, and (3) Support Vector Machines (SVMs) based classification. The key contribution in this work is to apply a sorting strategy to a universal yet information-preserving random(More)
This paper develops an efficient new method for 3D partial shape retrieval. First, a Monte Carlo sampling strategy is employed to extract local shape signatures from each 3D model. After vector quantization, these features are represented by using a bag-of-words model. The main contributions of this paper are threefold as follows: 1) a partial shape(More)
This paper presents a novel coarse-to-fine global localization approach inspired by object recognition and text retrieval techniques. Harris-Laplace interest points characterized by scale-invariant transformation feature descriptors are used as natural landmarks. They are indexed into two databases: a location vector space model (LVSM) and a location(More)
In this paper, we deal with the problem of detecting the existence and the location of salient objects for thumbnail images on which most search engines usually perform visual analysis in order to handle web-scale images. Different from previous techniques, such as sliding window-based or segmentation-based schemes for detecting salient objects, we propose(More)
Recently, manifold learning has been widely exploited in pattern recognition, data analysis, and machine learning. This paper presents a novel framework, called Riemannian manifold learning (RML), based on the assumption that the input high-dimensional data lie on an intrinsically low-dimensional Riemannian manifold. The main idea is to formulate the(More)
Lip reading from visual channels remains a challenging topic considering the various speaking characteristics. In this paper, we address an efficient lip reading approach by investigating the unsupervised random forest manifold alignment (RFMA). The density random forest is employed to estimate affinity of patch trajectories in speaking facial videos. We(More)
Segmenting images into superpixels as supporting regions for feature vectors and primitives to reduce computational complexity has been commonly used as a fundamental step in various image analysis and computer vision tasks. In this paper, we describe the structure-sensitive superpixel technique by exploiting Lloyd’s algorithm with the geodesic distance.(More)
In order to improve the stability of eye cursor, we introduce three methods, force field (FF), speed reduction (SR), and warping to target center (TC) to modulate eye cursor trajectories by counteracting eye jitter, which is the main cause of destabilizing the eye cursor. We evaluate these methods using two controlled experiments. One is an attention task(More)
Detecting anomalies in surveillance videos, that is, finding events or objects with low probability of occurrence, is a practical and challenging research topic in computer vision community. In this paper, we put forward a novel unsupervised learning framework for anomaly detection. At feature level, we propose a Sparse Semi-nonnegative Matrix Factorization(More)