Saliency detection using maximum symmetric surround

Achanta, Radhakrishna; Süsstrunk, Sabine

doi:10.1109/icip.2010.5652636

Cited by 338 publications

(293 citation statements)

References 16 publications

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“…The experimental results demonstrate the effectiveness of these features. In our implementation, we choose seven state-of-the-art methods, i.e., IT [1], GBVS [11], SR [12], CA [7], MSS [13], SER [14], and RC [3]. As shown in Fig.…”

Section: Low-level Featuresmentioning

confidence: 99%

Learning a Saliency Map for Fixation Prediction

Zeng

Wang

2013

IEICE Trans. Inf. & Syst.

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SUMMARYIn this letter, we use the saliency maps obtained by several bottom-up methods to learn a model to generate a bottom-up saliency map. In order to consider top-down image semantics, we use the high-level features of objectness and background probability to learn a top-down saliency map. The bottom-up map and top-down map are combined through a twolayer structure. Quantitative experiments demonstrate that the proposed method and features are effective to predict human fixation.

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Section: Low-level Featuresmentioning

confidence: 99%

Learning a Saliency Map for Fixation Prediction

Zeng

Wang

2013

IEICE Trans. Inf. & Syst.

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“…Figure 14 the distance measured between the saliency maps and ground truth data using EOBD. (Achanta, et al, 2009) HFT (Li, et al, 2007), MSSS (Achanta & Susstrunk, 2010 ) ISD…”

Section: ≤ ≤ 100mentioning

confidence: 99%

“…The algorithm has been applied on images from different datasets that are available online for saliency tests, such as the datasets in (Liu, 2007), (Judd, 2012), (Achanta, 2010), (Li, 2010), in additional to the HFT (Li, et al, 2007), Itti (Itti, et al, 1998), and MSSS (Achanta & Susstrunk, 2010 ).…”

Section: ≤ ≤ 100mentioning

confidence: 99%

Irregularity-based image regions saliency identification and evaluation

Al-Azawi

Yang

Istance

2014

Multimed Tools Appl

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IntroductionSalient points, objects, or regions extraction is still a challenging topic of research and its definition is still very general. Different literature have defined the saliency in different ways. In general and based on human vision system HVS, the salient object is the object that may capture the attention or attract the HVS. HVS uses two stages to identify the objects, preattentive and attentive stages (Kadir & Brady, 2001).In pre-attentive stage, the local regions of image that present spatial discontinuity are detected. In the attentive stage, relationships between these regions are found, and grouped together. The rest of the paper will be organized as follows: previous work is presented in section 2. In Section 3, we will explain the proposed saliency extraction technique based on the irregularity of the regions in an image. In section 44, a new automatic thresholding technique will be proposed. Points clustering and region merging will be discussed in section 5. Section 6 will review the saliency evaluation technique and introduce a new method that is suitable to the application at hand. In section 7 we will discuss the experimental results and the optimum window size selection. Finally, conclusions will be derived in section 8. 2 Previous WorkWavelet was used by many authors such as in (Loupias, et al., 2000), (Tian, et al., 2001), (Song, et al., 2006), (Lin & Yang, 2007), and (Arivazhagan & Shebiah, 2009) In this paper, we shall introduce a robust and general saliency extraction method that is fully automated and does not need any intervention from the user. In addition, it does need any pre-knowledge about the image. Irregularity as a Measure of SaliencyIn this Section, we shall discuss the proposed technique. First, we shall define the saliency as any irregular region in the image with nature differs from the nature of the image as a whole; for example, a region with texture in a uniform region, or vice versa, thus we shall define irregularity as the main measure of saliency.Let ℐ( , ) be the image intensity at position ( , ), where = 1, 2, … . , , = 1,2, … , , with and are the image width and height respectively. The image is represented as a set of pixels and is defined as:Where p xy is the pixel's value at location ( , ) and ℕ is the set of natural numbers. Furthermore, we shall define the set ℙ = { | ⊆ } as the power set of .According to the application at hands, the main constrain is that; all the elements of a given subset should belong to a connected region which means they should be in a given neighbouring area.The sub-image, which we shall refer to as a region, is a subset of the image with elements that belong to the same region. The size of this sub-image is × ℎ.Considering that the regions are disjoint, they can be defined as:Assuming that there is no overlapping between the adjacent regions, the total number of regions is equal to × ℎ . In most cases there should be overlapping between the moving windows, if we denote the overlapping value by , then the total numbers of wind...

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“…This method can provide pleasing results in most cases and it has the advantage of computational efficiency. As an extension to [12], Achanta et al improved their original method by considering the special effects of boundaries in their later work [13]. In [14], Cheng et al proposed a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence.…”

Section: Introductionmentioning

confidence: 99%

SDSP: A novel saliency detection method by combining simple priors

Zhang

2013

2013 IEEE International Conference on Image Processing

146

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Salient regions detection from images is an important and fundamental research problem in neuroscience and psychology and it serves as an indispensible step for numerous machine vision tasks. In this paper, we propose a novel conceptually simple salient region detection method, namely SDSP, by combining three simple priors. At first, the behavior that the human visual system detects salient objects in a visual scene can be well modeled by band-pass filtering. Secondly, people are more likely to pay their attention on the center of an image. Thirdly, warm colors are more attractive to people than cold colors are. Extensive experiments conducted on the benchmark dataset indicate that SDSP could outperform the other state-of-the-art algorithms by yielding higher saliency prediction accuracy. Moreover, SDSP has a quite low computational complexity, rendering it an outstanding candidate for time critical applications. The Matlab source code of SDSP and the evaluation results have been made online available at

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Saliency detection using maximum symmetric surround

Cited by 338 publications

References 16 publications

Learning a Saliency Map for Fixation Prediction

Learning a Saliency Map for Fixation Prediction

Irregularity-based image regions saliency identification and evaluation

SDSP: A novel saliency detection method by combining simple priors

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