Saliency from hierarchical adaptation through decorrelation and variance normalization

García-Díaz, Antón; Fdez-Vidal, Xosé R.; Pardo, Xosé M.; Dosil, Raquel

doi:10.1016/j.imavis.2011.11.007

Cited by 191 publications

(131 citation statements)

References 23 publications

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“…For our comparison, eleven state-of-the-art saliency models, namely, AIM by Bruce and Tsotsos [40], AWS by Garcia-Diaz et al [27], Erdem by Erdem and Erdem [22], Hou by Hou and Zhang [41], Spec by Schauerte and Stiefelhagen [42], GBA by Alsam et al [24,25], fast GBA proposed in this paper (S f = 0.5, N r = 3, b = 22; for details, please see Section 4.6), GBVS by Harel et al [43], Itti by Itti et al [26], Judd by Judd et al [16] and LG by Borji and Itti [44] are used. In line with the study by Borji et al [45], two models are selected to provide a baseline for the evaluation.…”

Section: Saliency Modelsmentioning

confidence: 99%

Modeling Bottom-Up Visual Attention Using Dihedral Group D4

Sharma

2016

Symmetry

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Abstract:In this paper, first, we briefly describe the dihedral group D 4 that serves as the basis for calculating saliency in our proposed model. Second, our saliency model makes two major changes in a latest state-of-the-art model known as group-based asymmetry. First, based on the properties of the dihedral group D 4 , we simplify the asymmetry calculations associated with the measurement of saliency. This results is an algorithm that reduces the number of calculations by at least half that makes it the fastest among the six best algorithms used in this research article. Second, in order to maximize the information across different chromatic and multi-resolution features, the color image space is de-correlated. We evaluate our algorithm against 10 state-of-the-art saliency models. Our results show that by using optimal parameters for a given dataset, our proposed model can outperform the best saliency algorithm in the literature. However, as the differences among the (few) best saliency models are small, we would like to suggest that our proposed model is among the best and the fastest among the best. Finally, as a part of future work, we suggest that our proposed approach on saliency can be extended to include three-dimensional image data.

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Section: Saliency Modelsmentioning

confidence: 99%

Modeling Bottom-Up Visual Attention Using Dihedral Group D4

Sharma

2016

Symmetry

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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%

“…Different algorithms are used to train the best way to combine together saliency maps. (c) Itti [6] (d) Le Meur [4] (e) GBVS [3] (f) Hou [8] (g) Bruce [7] (h) Judd [14] (i) AWS [5] (j) RARE2012 [15] …”

Section: Context and Problemmentioning

confidence: 99%

“…3.1 Performance of state-of-the-art models Figure 2 illustrates the performance of the 8 selected models (GBVS [3], Judd [14], RARE2012 [15], AWS [5], Le Meur [4], Bruce [7], Hou [8] and Itti [6]) over Bruce and Judd datasets. According to our results, we find that the top 2 models are GBVS and RARE2012, the top 4 models are GBVS, RARE2012, Judd and AWS.…”

Section: Performancementioning

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%

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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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Computational Models of Visual Attention and Applications

Meur

Mancaş

2015

Biologically Inspired Computer Vision

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Saliency from hierarchical adaptation through decorrelation and variance normalization

Cited by 191 publications

References 23 publications

Modeling Bottom-Up Visual Attention Using Dihedral Group D4

Modeling Bottom-Up Visual Attention Using Dihedral Group D4

Saliency Aggregation: Does Unity Make Strength?

Computational Models of Visual Attention and Applications

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