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The goal of objectness estimation is to predict a moderate number of proposals of all possible objects in a given image with high efficiency. Most existing works solve this problem solely in conventional 2D color images. In this paper, we demonstrate that the depth information could benefit the estimation as a complementary cue to color information. After detailed analysis of depth characteristics,...
In multi-label classification, labels often have correlations with each other. Exploiting label correlations can improve the performances of classifiers. Current multi-label classification methods mainly consider the global label correlations. However, the label correlations may be different over different data groups. In this paper, we propose a simple and efficient framework for multi-label classification,...
In voice conversion, sparse-representation-based methods have recently been garnering attention because they are, relatively speaking, not affected by over-fitting or over-smoothing problems. In these approaches, voice conversion is achieved by estimating a sparse vector that determines which dictionaries of the target speaker should be used, calculated from the matching of the input vector and dictionaries...
Recently, local sparse representation (LSR) has been successfully applied in visual tracking, owing to its discriminative nature and robustness against local noise and occlusions. It is note worthy that local sparse codes computed with a template form a 3-order tensor of their original layout, although most pooling operators convert it to a vector by concatenating or computing statistics on it. As...
We present a robust system for geolocalization in dynamic environments. Our application is a camera system designed to help the visually impaired navigate. It is also suitable for healthy eyesight users to find their way around unfamiliar areas. We combine visual odometry (VO) with the semantic information available in map to estimate the global coordinates of the walking users. In order to handle...
Automatically describing the content of an image is a challenging task in artificial intelligence. The difficulty is particularly pronounced in activity recognition and the image caption revealed by the relationship analysis of the activities involved in the image. This paper presents a unified hierarchical model to model the interaction activity between human and nearby object, and then speculates...
We proposed a compact color descriptor specialized in expressing clothing for person re-identification across cameras. It expresses any type of clothing colors by combination of a set of unicolor clothing selected from a collection of various colored clothing widely and densely spread over clothing color space; which is called a wardrobe. Proper wardrobe can be collected through a clothing manufacture...
Foreground detection with dynamic background is a challenging task in video surveillance analysis. When clean background bases are constructed, regression based foreground detection usually becomes more effective. In this paper, a novel basis selection method based on local neighborhood structure is proposed. The present method first constructs local neighborhood relationships among the basis candidates...
Image retargeting attempts to adapt images to different devices while preserving the salient contents. Most existing methods address retargeting of a single image. In this paper, we propose a novel image retargeting method for resizing a pair of stereo images. Naively retargeting each image independently will distort the geometric structure and will impair the perception of the 3-D structure of the...
For music identification, conventional bag of audio words model methods generally compute a histogram for a piece of music, which ignores the temporal characteristic of music and has a negative influence on the accuracy. In addition, they are usually based on DFT spectrogram, which cannot represent music as well as Constant Q (CQ) spectrogram. To address the above problems, we propose a two-layer...
Due to the explosive growth of visual data and the raised urgent needs for more efficient nearest neighbor search methods, hashing methods have been widely studied in recent years. However, parameter optimization of the hash function in most available approaches is tightly coupled with the form of the function itself, which makes the optimization difficult and consequently affects the similarity preserving...
Image super-resolution with sparsity prior provides promising performance. However, traditional sparse-based super resolution methods transform a two dimensional (2D) image into a one dimensional (1D) vector, which ignores the intrinsic 2D structure as well as spatial correlation inherent in images. In this paper, we propose the first image super-resolution method which reconstructs a high resolution...
We analyze the problem of temporally consistent video exposure correction. Existing methods usually either fail to evaluate optimal exposure for every region or cannot get temporally consistent correction results. In addition, the contrast is often lost when the detail is not preserved properly during correction. In this paper, we use the block-based energy minimization to evaluate the temporally...
This paper proposes a novel scheme for the joint compression of photo collections framing the same object or scene. The proposed approach starts by locating corresponding features in the various images and then exploits a Structure from Motion algorithm to estimate the geometric relationships between the various images and their viewpoints. Then it uses 3D information and warping to predict images...
RGB-D cameras have enabled real-time 3D video processing for numerous computer vision applications, especially for surveillance type applications. In this paper, we first present a real-time anti-forensic 3D object stream manipulation framework to capture and manipulate live RBG-D data streams to create realistic images/videos showing individuals performing activities they did not actually do. The...
The recommendation of trending images has become a popular feature used by commercial search engines to attract public attention. By browsing through trending images, search engine users can discover trending events at a glance. However, the selection of trending images is very challenging and remains an open issue. Most existing work is highly dependent on editorial efforts, though some preliminarily...
Sculpture design is challenging due to its inherent difficulty in characterizing an artwork quantitatively, and few works have been done to assist sculpture design. We present a novel platform to help sculptors in two stages, comprising automatic sculpture reconstruction and free spectral-based sculpture pose editing. During sculpture reconstruction, we co-segment a sculpture from real scene images...
This paper proposes a superpixel tracking method via a graph-based hybrid discriminative-generative appearance model. By utilizing a superpixel-based graph structure as the visual representation, spatial information between superpixels is considered. For constructing the discriminative appearance model, we propose a graph-based semi-supervised support vector machine (SVM) approach by taking superpixels...
To facilitate efficiency, most recent successful saliency detection methods are built on superpixel level. However, saliency detection with single-scale superpixel segmentation may fail in capturing the intrinsic salient objects in complex natural scenes with small-scale high-contrast backgrounds. To tackle this problem and realize more reliable saliency detection, we present a simple strategy using...
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