Human visual system model

About: Human visual system model is a research topic. Over the lifetime, 8697 publications have been published within this topic receiving 259440 citations.

Does where you Gaze on an Image Affect your Perception of Quality? Applying Visual Attention to Image Quality Metric

Abstract: The aim of an objective image quality assessment is to find an automatic algorithm that evaluates the quality of pictures or video as a human observer would do. To reach this goal, researchers try to simulate the Human Visual System (HVS). Visual attention is a main feature of the HVS, but few studies have been done on using it in image quality assessment. In this work, we investigate the use of the visual attention information in their final pooling step. The rationale of this choice is that an artefact is likely more annoying in a salient region than in other areas. To shed light on this point, a quality assessment campaign has been conducted during which eye movements have been recorded. The results show that applying the visual attention to image quality assessment is not trivial, even with the ground truth.

Vision in dogs.

Abstract: Compared with the visual system in human beings, the canine visual system could be considered inferior in such aspects as degree of binocular overlap, color perception, accommodative range, and visual acuity. However, in other aspects of vision, such as ability to function in dim light, rapidity with which the retina can respond to another image (flicker fusion), field of view, ability to differentiate shades of gray, and perhaps, ability to detect motion, the canine visual system probably surpasses the human visual system. This has made the dog a more efficient predator in certain environmental situations and permits it to exploit an ecological niche inaccessible to humans.

Visual language modeling for image classification

Abstract: Although it has been studied for many years, image classification is still a challenging problem. In this paper, we propose a visual language modeling method for content-based image classification. It transforms each image into a matrix of visual words, and assumes that each visual word is conditionally dependent on its neighbors. For each image category, a visual language model is constructed using a set of training images, which captures both the co-occurrence and proximity information of visual words. According to how many neighbors are taken in consideration, three kinds of language models can be trained, including unigram, bigram and trigram, each of which corresponds to a different level of model complexity. Given a test image, its category is determined by estimating how likely it is generated under a specific category. Compared with traditional methods that are based on bag-of-words models, the proposed method can utilize the spatial correlation of visual words effectively in image classification. In addition, we propose to use the absent words, which refer to those appearing frequently in a category but not in the target image, to help image classification. Experimental results show that our method can achieve comparable accuracy while performing classification much more quickly.

Visual Saliency Detection via Sparsity Pursuit

Abstract: Saliency mechanism has been considered crucial in the human visual system and helpful to object detection and recognition. This paper addresses a novel feature-based model for visual saliency detection. It consists of two steps: first, using the learned overcomplete sparse bases to represent image patches; and then, estimating saliency information via low-rank and sparsity matrix decomposition. We compare our model with the previous methods on natural images. Experimental results on both natural images and psychological patterns show that our model performs competitively for visual saliency detection task, and suggest the potential application of matrix decomposition and convex optimization for image analysis.

A model of motion attention for video skimming

Abstract: One of the key issues in video manipulation is video abstraction in the form of skimmed video. For this purpose, an important task is to determine the content significance of each chunk of frames in a video sequence. In this paper, we present a new computational model of motion attention and the approach to applying this model in video skimming. The effectiveness of our architecture and model is demonstrated by user studies of visual skimming experiments. The results indicate that the precision of motion attention detection is over 80%, and the user satisfaction of visual skimming is beyond 70%.