Bottom-Up Saliency Detection Model Based on Human Visual Sensitivity and Amplitude Spectrum

Fang, Yuming; Wang, Lin; Lee, Bu-Sung; Lau, Chiew Tong; Chen, Zhenzhong; Lin, Chia-Wen

doi:10.1109/tmm.2011.2169775

Cited by 154 publications

(79 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By this way, the analysis on the saliency model can be observed quantitatively with the integration of PCA and m-PCNN to the WT based local saliency [14] and to the model in [12] as well. For this purpose, a subset of the Microsoft Image database (500 images) [21,10] is used for experimental analysis. The dataset includes images and their respective ground truths from various subjects [21,10].…”

Section: Resultsmentioning

confidence: 99%

“…For this purpose, a subset of the Microsoft Image database (500 images) [21,10] is used for experimental analysis. The dataset includes images and their respective ground truths from various subjects [21,10]. The saliency maps obtained from the local saliency map in [14] and from the proposed model are also converted to binary maps for comparison to the ground truth in order to obtain a quantitative analysis.…”

Section: Resultsmentioning

confidence: 99%

“…Saliency computation is one of the recent research areas evolved from the concept of visual attention, especially on the human visual system [8,9]. The visual attention structure provides features by selecting significant parts and reducing the redundant data from the perceived visual scene [10]. This property of the visual attention has been attracting researchers' attention to create computational models for computer vision applications [11,12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Saliency for RGB-D Visual Tracking and Control Strategies for a Bio-monitoring Mobile Robot

İmamoğlu

Wei

Shi

et al. 2013

Communications in Computer and Information Science

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Saliency for RGB-D Visual Tracking and Control Strategies for a Bio-monitoring Mobile Robot

İmamoğlu

Wei

Shi

et al. 2013

Communications in Computer and Information Science

View full text Add to dashboard Cite

“…Frequency domain was used to extract the saliency of the objects by many literature such as (Bruce, et al, 2007), (Hou & Zhang, 2007), (Li, et al, 2007), (Achanta, et al, 2009), (Zhou, et al, 2010), and (Fang, et al, 2012). The idea behind using the frequency domain is that, they considered that salient points usually of higher change in frequency domain both in magnitude and in orientation.…”

Section: Previous Workmentioning

confidence: 99%

“…Tatler supported the idea of empirical evaluation of the saliency since they were interested more in mimicking the natural behaviour of the humans' eyes (Tatler, et al, 2011). Precision and Recall, is another method was used by many publications such as (Fang, et al, 2012), (Stöttinger , et al, 2009), (Liu, et al, 2011), (Achanta, et al, 2009), and (Anon., 2013) to evaluate the saliency algorithms.…”

Section: Saliency Evaluationmentioning

confidence: 99%

Irregularity-based image regions saliency identification and evaluation

Al-Azawi

Yang

Istance

2014

Multimed Tools Appl

View full text Add to dashboard Cite

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...

show abstract

Introduction to Visual Attention

Zhang

Wang

2013

Selective Visual Attention

View full text Add to dashboard Cite

Bottom-Up Saliency Detection Model Based on Human Visual Sensitivity and Amplitude Spectrum

Cited by 154 publications

References 40 publications

An Improved Saliency for RGB-D Visual Tracking and Control Strategies for a Bio-monitoring Mobile Robot

An Improved Saliency for RGB-D Visual Tracking and Control Strategies for a Bio-monitoring Mobile Robot

Irregularity-based image regions saliency identification and evaluation

Introduction to Visual Attention

Contact Info

Product

Resources

About