Towards context-sensitive visual attention (original) (raw)
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Computational visual attention systems and their cognitive foundations
ACM Transactions on Applied Perception, 2010
Based on concepts of the human visual system, computational visual attention systems aim to detect regions of interest in images. Psychologists, neurobiologists, and computer scientists have investigated visual attention thoroughly during the last decades and profited considerably from each other. However, the interdisciplinarity of the topic holds not only benefits but also difficulties: Concepts of other fields are usually hard to access due to differences in vocabulary and lack of knowledge of the relevant literature. This article aims to bridge this gap and bring together concepts and ideas from the different research areas. It provides an extensive survey of the grounding psychological and biological research on visual attention as well as the current state of the art of computational systems. Furthermore, it presents a broad range of applications of computational attention systems in fields like computer vision, cognitive systems, and mobile robotics. We conclude with a discus...
A Computational-Cognitive Model of Visual Attention in Dynamic Environments
2022
Background and Objectives: Visual attention is a high-order cognitive process of the human brain that defines where a human observer attends. Dynamic computational visual attention models are modeled on the behavior of the human brain. They can predict what areas a human will pay attention to when viewing a scene such as a video. However, several types of computational models have been proposed to provide a better understanding of saliency maps in static and dynamic environments; most of these models are used for specific scenes. In this paper, we propose a model that can generate saliency maps in various dynamic environments with complex scenes. Methods: We used a deep learner as a mediating network to combine basic saliency maps with appropriate weighting. Each of these basic saliency maps covers an essential feature of human visual attention, and ultimately the final saliency map is very similar to human visual behavior. Results: The proposed model is run on two datasets, and the generated saliency maps are evaluated by different criteria such as ROC, CC, NSS, SIM, and KLdiv. The results show that the proposed model has a good performance compared to other similar models. Conclusion: The proposed model consists of three main parts, including basic saliency maps, gating network, and combinator. This model was implemented on the ETMD dataset, and the resulting saliency maps (visual attention areas) were compared with some other models in this field by evaluation criteria, and their results were evaluated. The results obtained from the proposed model are acceptable, and based on the accepted evaluation criteria in this area; it performs better than similar models.
International journal of neural systems, 2007
Selective Tuning (ST) presents a framework for modeling attention and in this work we show how it performs in covert visual search tasks by comparing its performance to human performance. Two implementations of ST have been developed. The Object Recognition Model recognizes and attends to simple objects formed by the conjunction of various features and the Motion Model recognizes and attends to motion patterns. The validity of the Object Recognition Model was first tested by successfully duplicating the results of Nagy and Sanchez. A second experiment was aimed at an evaluation of the model's performance against the observed continuum of search slopes for feature-conjunction searches of varying difficulty. The Motion Model was tested against two experiments dealing with searches in the visual motion domain. A simple odd-man-out search for counter-clockwise rotating octagons among identical clockwise rotating octagons produced linear increase in search time with the increase of s...
Experimenting a Visual Attention Model in the Context of CBIR Systems
2013
Many novel applications in the field of object recognition and pose estimation have been built relying on local invariant features extracted from selected key points of the images. Such keypoints usually lie on high-contrast regions of the image, such as object edges. However, the visual saliency of the those regions is not considered by state-of-the art detection algorithms that assume the user is interested in the whole image. Moreover, the most common approaches discard all the color information by limiting their analysis to monochromatic versions of the input images. In this paper we present the experimental results of the application of a biologically-inspired visual attention model to the problem of local feature selection in landmark and object recognition tasks. The model uses color-information and restricts the matching between the images to the areas showing a strong saliency. The results show that the approach improves the accuracy of the classifier in the object recognit...