Saliency, attention, and visual search: An information theoretic approach

Bruce, Neil D. B.

doi:10.1167/9.3.5

Cited by 745 publications

(634 citation statements)

References 73 publications

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“…Receiver operating characteristic (ROC) curve (Bruce and Tsotsos 2009) is usually used to evaluate the effectiveness of detection. This curve pays attention to the judgment of the existence of a target not the location of it.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Airport detection in remote sensing images: a method based on saliency map

Wang

et al. 2012

Cogn Neurodyn

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The detection of airport attracts lots of attention and becomes a hot topic recently because of its applications and importance in military and civil aviation fields. However, the complicated background around airports brings much difficulty into the detection. This paper presents a new method for airport detection in remote sensing images.Distinct from other methods which analyze images pixel by pixel, we introduce visual attention mechanism into detection of airport and improve the efficiency of detection greatly. Firstly, Hough transform is used to judge whether an airport exists in an image. Then an improved graphbased visual saliency model is applied to compute the saliency map and extract regions of interest (ROIs). The airport target is finally detected according to the scaleinvariant feature transform features which are extracted from each ROI and classified by hierarchical discriminant regression tree. Experimental results show that the proposed method is faster and more accurate than existing methods, and has lower false alarm rate and better antinoise performance simultaneously.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Airport detection in remote sensing images: a method based on saliency map

Wang

et al. 2012

Cogn Neurodyn

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“…To investigate this point, we select 8 state-of-the-art models (GBVS [3], Judd [14], RARE2012 [15], AWS [5], Le Meur [4], Bruce [7], Hou [8] and Itti [6]) and aggregate their saliency maps into a unique one. The following subsections present the tested aggregation methods.…”

Section: Context and Problemmentioning

confidence: 99%

“…The discrepancies are related to the quality of the prediction but also to the saliency map representation. Indeed some models output very focused saliency maps [3][4][5] whereas the distribution of saliency values is much more uniform in other models [6,7]. Others tend to emphasize more on the image edges [8], the color or luminance contrast.…”

Section: Introductionmentioning

confidence: 99%

Saliency Aggregation: Does Unity Make Strength?

Meur

2015

Computer Vision -- ACCV 2014

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Abstract. In this study, we investigate whether the aggregation of saliency maps allows to outperform the best saliency models. This paper discusses various aggregation methods; six unsupervised and four supervised learning methods are tested on two existing eye fixation datasets. Results show that a simple average of the TOP 2 saliency maps significantly outperforms the best saliency models. Considering more saliency models tends to decrease the performance, even when robust aggregation methods are used. Concerning the supervised learning methods, we provide evidence that it is possible to further increase the performance, under the condition that an image similar to the input image can be found in the training dataset. Our results might have an impact for critical applications which require robust and relevant saliency maps.

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“…For each psychophysical pattern, we calculate the saliency maps for PCT, P 2 CA and 3 stateof-the-art attention models: PQFT from Guo and Zhang (2010), AIM from Bruce and Tsotsos (2009), and ITTI from Itti et al (1998). The results are shown in Figs.…”

Section: Psychophysical Consistencymentioning

confidence: 99%

“…Therefore, our P 2 CA model has a computational complexity of O ((MN) 2 ). The computational procedures of AIM and ITTI are comparatively complex (see Bruce and Tsotsos 2009;Itti et al 1998), and some computational details were not shown in literature, but in their toolboxes that are coded in Matlab. Therefore, it is difficult for us to give a precise computational complexity of these two models.…”

Section: Computational Costmentioning

confidence: 99%

Bottom–up attention: pulsed PCA transform and pulsed cosine transform

Wang

Zhang

2011

Cogn Neurodyn

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In this paper we propose a computational model of bottom-up visual attention based on a pulsed principal component analysis (PCA) transform, which simply exploits the signs of the PCA coefficients to generate spatial and motional saliency. We further extend the pulsed PCA transform to a pulsed cosine transform that is not only data-independent but also very fast in computation. The proposed model has the following biological plausibilities. First, the PCA projection vectors in the model can be obtained by using the Hebbian rule in neural networks. Second, the outputs of the pulsed PCA transform, which are inherently binary, simulate the neuronal pulses in the human brain. Third, like many Fourier transform-based approaches, our model also accomplishes the cortical center-surround suppression in frequency domain. Experimental results on psychophysical patterns and natural images show that the proposed model is more effective in saliency detection and predict human eye fixations better than the state-of-the-art attention models.

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Saliency, attention, and visual search: An information theoretic approach

Cited by 745 publications

References 73 publications

Airport detection in remote sensing images: a method based on saliency map

Airport detection in remote sensing images: a method based on saliency map

Saliency Aggregation: Does Unity Make Strength?

Bottom–up attention: pulsed PCA transform and pulsed cosine transform

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