Learn More
Although depth information plays an important role in the human vision system, it is not yet well-explored in existing visual saliency computational models. In this work, we first introduce a large scale RGBD image dataset to address the problem of data deficiency in current research of RGBD salient object detection. To make sure that most existing RGB(More)
The technique of support vector regression is applied to the problem of estimating the chromaticity of the light illuminating a scene from a color histogram of an image of the scene. Illumination estimation is fundamental to white balancing digital color images and to understanding human color constancy. Under controlled experimental conditions, the support(More)
The retinex algorithm for lightness and color constancy is extended to include 3-dimensional spatial information reconstructed from a stereo image. A key aspect of traditional retinex is that, within each color channel, it makes local spatial comparisons of intensity. In particular, intensity ratios are computed between neighboring spatial locations,(More)
Thin-plate spline interpolation is used to interpolate the chromaticity of the color of the incident scene illumination across a training set of images. Given the image of a scene under unknown illumination, the chromaticity of the scene illumination can be found from the interpolated function. The resulting illumination-estimation method can be used to(More)
Salient object detection provides an alternative solution to various image semantic understanding tasks such as object recognition, adaptive compression and image retrieval. Recently, low-rank matrix recovery (LR) theory has been introduced into saliency detection, and achieves impressed results. However, the existing LR-based models neglect the underlying(More)
Color constancy is an important perceptual ability of humans to recover the color of objects invariant of light information. It is also necessary for a robust machine vision system. Until now, a number of color constancy algorithms have been proposed in the literature. In particular, the edge-based color constancy uses the edge of an image to estimate light(More)
—Along with the ever-growing Web, horror contents sharing in the Internet has interfered with our daily life and affected our, especially children's, health. Therefore horror image recognition is becoming more important for web objectionable content filtering. This paper presents a novel context-aware multi-instance learning (CMIL) model for this task. This(More)
The key to automatic white balancing of digital imagery is to estimate accurately the color of the overall scene illumination. Many methods for estimating the illumination's color have been proposed [1-6]. Although not the most accurate, one of the simplest and quite widely used methods is the gray world algorithm [6]. Borrowing on some of the strengths and(More)
Light color estimation is crucial to the color constancy problem. Past decades have witnessed great progress in solving this problem. Contrary to traditional methods, many researchers propose a variety of combinational color constancy methods through applying different color constancy mathematical models on an image simultaneously and then give out a final(More)
In image understanding, the automatic recognition of emotion in an image is becoming important from an applicative viewpoint. Considering the fact that the emotion evoked by an image is not only from its global appearance but also interplays among local regions, we propose a novel context-aware classification model based on bilayer sparse representation(More)