Assessing the contribution of color in visual attention

Jost, Timothée; Ouerhani, Nabil; Wartburg, Roman von; Müri, René M.; Hügli, Heinz

doi:10.1016/j.cviu.2004.10.009

Cited by 118 publications

(68 citation statements)

References 21 publications

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“…Recently the models have been used to boost some computer vision and pattern recognition techniques such as object detection [113], [124], [125], object recognition [126]- [131], action recognition [132], [133], segmentation [37], [114], [115], [134], [135] and background subtraction [136]. Besides, specific applications include video summarization [137] and compression [138], scene understanding [139]- [141], computer-human interaction [98], [142]- [147], robotics [132], [148]- [150], and driver assistance [151], [152]. The potential of the visual attention models that are capable of extracting important regions promises their contributions to many other domains.…”

Section: Discussionmentioning

confidence: 99%

Computational Models of Human Visual Attention and Their Implementations: A Survey

Kimura

Yonetani

Hirayama

2013

IEICE Trans. Inf. & Syst.

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SUMMARYWe humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation. key words : human visual attention, computational model, saliency, bottom-up, top-down MotivationDeveloping sophisticated algorithms for detecting and recognizing something like objects from a given image and video has been a long distance challenge in pattern recognition and computer vision research fields. In fact, a huge number of studies, techniques and theories related to object detection and recognition have already been developed. In particular, several methods for detecting certain specific categories of objects such as human bodies and human faces have already been put to practical use in for example surveillance, authentication and the human-centric enhancement of image quality, with the best possible use of the prior knowledge of target objects (human bodies and faces) [1], [2]. However, generic object detection and recognition without any constraints as regards the target objects has remained major challenge, because (1) various kinds of objects might constitute the targets and (2) target objects in the same category might have different appearances due to variations of instances in a specific category, illumination changes and so on. † † The author is with the Graduate School of Informatics, Kyoto University, Kyoto-shi, 606-8501 Japan.† † † The author is with the Graduate School of Information Science, Nagoya University, Nagoya-shi, 464-8603 Japan.a) E-mail: akisato@ieee.org b) E-mail: yonetani@vision.kuee.kyoto-u.ac.jp c) E-mail: hirayama@is.nagoya-u.ac.jp DOI: 10.1587/transinf.E96.D.562 On the other hand, human beings seem to be able to detect various kinds of objects without any thought or effort. For example, from Fig. 1 left, we can easily and instantly detect a red car, a blue traffic sign and a broad white line. Visual attention [3] is considered to play an important role in achieving this function. Visual attention is one of the built-in mechanisms of the human visual system that quickly selects regions in a visual scene, which are most likely to contain items of interest. Such a pre-selection mechanism focusing only on relevant data would be essential in enabling computers to undertake subsequent processing such as generic o...

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Section: Discussionmentioning

confidence: 99%

Computational Models of Human Visual Attention and Their Implementations: A Survey

Kimura

Yonetani

Hirayama

2013

IEICE Trans. Inf. & Syst.

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“…Then, a comparison of these two models with the whole set of human fixation patterns was performed in order to obtain the respective scores. Note that the score s was computed taking the first 5 fixations of each subject into account, since it has been suggested that, with regard to human observers, initial fixations are controlled mainly in a bottom-up manner [10]. Figure 4 shows the scores for the different individual images.…”

Section: Performance In Presence Of 2d Imagesmentioning

confidence: 99%

“…The eye tracking data was parsed for fixations and saccades in real time, using parsing parameters proven to be useful for cognitive research thanks to the reduction of detected microsaccades and short fixations (< 100 ms). Remaining saccades with amplitudes less than 20 pixels (0.75 o visual angle) as well as fixations shorter than 120 ms were discarded after-wards [10].…”

Section: Eye Movement and Fixation Pattern Recordingmentioning

confidence: 99%

“…Using eye movement recording techniques to measure human visual attention, the authors report a relatively high correlation between human attention and the saliency map, especially when the images are presented for a relatively short time of few seconds. Jost et al [10] run similar experiments on a much larger number of test persons and could measure the quantitative improvement of the model when chromaticity channels are added to the conventional monochrome video channels. Visual attention in 3D scenes was first considered in [11] and recently, a visual attention model for 3D was quantitatively analyzed in presence of various synthetic and natural scenes [12].…”

Section: Introductionmentioning

confidence: 99%

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Model Performance for Visual Attention in Real 3D Color Scenes

Hügli

Jost

Ouerhani

2005

Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach

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Abstract. Visual attention is the ability of a vision system, be it biological or artificial, to rapidly detect potentially relevant parts of a visual scene. The saliency-based model of visual attention is widely used to simulate this visual mechanism on computers. Though biologically inspired, this model has been only partially assessed in comparison with human behavior. The research described in this paper aims at assessing its performance in the case of natural scenes, i.e. real 3D color scenes. The evaluation is based on the comparison of computer saliency maps with human visual attention derived from fixation patterns while subjects are looking at the scenes. The paper presents a number of experiments involving natural scenes and computer models differing by their capacity to deal with color and depth. The results point on the large range of scene specific performance variations and provide typical quantitative performance values for models of different complexity.

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“…As with the Human Visual System, the fixation frequency map was next filtered by a spatial Gaussian filter. These frequency maps were filtered by a spatial Gaussian filter of r = 37, which was chosen to approximate the size of the viewing field corresponding to the fovea in the gaze map [48]. The size of the Gaussian window was of 40 9 40 pixels.…”

Section: Comparisons Between Gaze Maps and Saliency Mapsmentioning

confidence: 99%

A Spatiotemporal Saliency Model for Video Surveillance

et al. 2011

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A video sequence is more than a sequence of still images. It contains a strong spatial-temporal correlation between the regions of consecutive frames. The most important characteristic of videos is the perceived motion foreground objects across the frames. The motion of foreground objects dramatically changes the importance of the objects in a scene and leads to a different saliency map of the frame representing the scene. This makes the saliency analysis of videos much more complicated than that of still images. In this paper, we investigate saliency in video sequences and propose a novel spatiotemporal saliency model devoted for video surveillance applications. Compared to classical saliency models based on still images, such as Itti's model, and space-time saliency models, the proposed model is more correlated to visual saliency perception of surveillance videos. Both bottom-up and topdown attention mechanisms are involved in this model. Stationary saliency and motion saliency are, respectively, analyzed. First, a new method for background subtraction and foreground extraction is developed based on content analysis of the scene in the domain of video surveillance. Then, a stationary saliency model is setup based on multiple features computed from the foreground. Every feature is analyzed with a multi-scale Gaussian pyramid, and all the features conspicuity maps are combined using different weights. The stationary model integrates faces as a supplement feature to other low level features such as color, intensity and orientation. Second, a motion saliency map is calculated using the statistics of the motion vectors field. Third, both motion saliency map and stationary saliency map are merged based on center-surround framework defined by an approximated Gaussian function. The video saliency maps computed from our model have been compared to the gaze maps obtained from subjective experiments with SMI eye tracker for surveillance video sequences. The results show strong correlation between the output of the proposed spatiotemporal saliency model and the experimental gaze maps.

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Assessing the contribution of color in visual attention

Abstract: Abstract

Cited by 118 publications

References 21 publications

Computational Models of Human Visual Attention and Their Implementations: A Survey

Computational Models of Human Visual Attention and Their Implementations: A Survey

Model Performance for Visual Attention in Real 3D Color Scenes

A Spatiotemporal Saliency Model for Video Surveillance

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