Visual Saliency Based on Conditional Entropy

Li, Yin; Zhou, Yue; Yan, Junchi; Niu, Zhibin; Yang, Jie

doi:10.1007/978-3-642-12307-8_23

Cited by 55 publications

(31 citation statements)

References 21 publications

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“…Wang & Li [75] extended the residual spectral approach by adding features based on gestalt principles to detect similarity and continuity. Li et al [76] proposed a bottom up approach for saliency detection, and they demonstrated a convolution of the image amplitude spectrum with a low pass Gaussian kernel of appropriate size that is equivalent to a saliency detector. Besides the amplitude spectrum, Guo et al [77] pointed out that the phase spectrum is the key to saliency modeling in the frequency domain.…”

Section: ) Bayesian Modelsmentioning

confidence: 99%

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A Comparison Study of Saliency Models for Fixation Prediction on Infants and Adults

Mahdi

Schlesinger

et al. 2018

IEEE Trans. Cogn. Dev. Syst.

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Abstract-Various saliency models have been developed over the years. The performance of saliency models is typically evaluated based on databases of experimentally recorded adult eye fixations. Although studies on infant gaze patterns have attracted much attention recently, saliency based models have not been widely applied for prediction of infant gaze patterns. In this study, we conduct a comprehensive comparison study of eight state-ofthe-art saliency models on predictions of experimentally captured fixations from infants and adults. Seven evaluation metrics are used to evaluate and compare the performance of saliency models. The results demonstrate a consistent performance of saliency models predicting adult fixations over infant fixations in terms of overlap, center fitting, intersection, information loss of approximation, and spatial distance between the distributions of saliency map and fixation map. In saliency and baselines models performance ranking, the results show that GBVS and Itti models are among the top three contenders, infants and adults have bias toward the centers of images, and all models and the center baseline model outperformed the chance baseline model.

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Section: ) Bayesian Modelsmentioning

confidence: 99%

“…6) Information theory models: Models in this category measure irregularity in image locations by maximizing the information sampled from one's environment [85] [86][87] [76]. Such models select the most informative locations and discard redundancies.…”

Section: ) Bayesian Modelsmentioning

confidence: 99%

A Comparison Study of Saliency Models for Fixation Prediction on Infants and Adults

Mahdi

Schlesinger

et al. 2018

IEEE Trans. Cogn. Dev. Syst.

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“…In [37], local entropy is suggested as a means to find salient image patches. Inspired by biological vision [38] defines saliency as the minimum uncertainty under perceptional distortion associated with an image region given its local neighborhood. This uncertainty is quantified in terms of the conditional entropy; computing which is difficult in practice.…”

Section: Information Theory and Saliencymentioning

confidence: 99%

Action recognition using global spatio-temporal features derived from sparse representations

Somasundaram

Cherian

Morellas

et al. 2014

Computer Vision and Image Understanding

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Recognizing actions is one of the important challenges in computer vision with respect to video data, with applications to surveillance, diagnostics of mental disorders, and video retrieval. Compared to other data modalities such as documents and images, processing video data demands orders of magnitude higher computational and storage resources. One way to alleviate this difficulty is to focus the computations to informative (salient) regions of the video. In this paper, we propose a novel global spatio-temporal selfsimilarity measure to score saliency using the ideas of dictionary learning and sparse coding. In contrast to existing methods that use local spatio-temporal feature detectors along with descriptors (such as HOG, HOG3D, HOF, etc.), dictionary learning helps consider the saliency in a global setting (on the entire video) in a computationally efficient way. We consider only a small percentage of the most salient (least self-similar) regions found using our algorithm, over which spatio-temporal descriptors such as HOG and region covariance descriptors are computed. The ensemble of such block descriptors in a bag-of-features framework provides a holistic description of the motion sequence which can be used in a classification setting. Experiments on several benchmark datasets in video based action classification demonstrate that our approach performs competitively to the state of the art.

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“…The level of saliency is finally computed as the negative log likelihood (self information) of the basis coefficient. Several similar models have also been presented by Renninger et al [70] and Li et al [71]. Furthermore, as an extended variant of those approaches, Sun et al [72], [73] recently proposed a framework for modeling saccadic eye movements via onthe-fly FastICA [74]- [76].…”

Section: Knowledge-based Modelsmentioning

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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Visual Saliency Based on Conditional Entropy

Cited by 55 publications

References 21 publications

A Comparison Study of Saliency Models for Fixation Prediction on Infants and Adults

A Comparison Study of Saliency Models for Fixation Prediction on Infants and Adults

Action recognition using global spatio-temporal features derived from sparse representations

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

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