Unsupervised visual saliency detection via information content measuring

Wu, Di; Sun, Xiudong; Jiang, Yongyuan; Hou, Chunfeng

doi:10.1049/el.2012.3343

Cited by 2 publications

(4 citation statements)

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“…In addition, the background regions frequently have similar appearance as the background prototypes. Therefore, the saliency map of R i is finally computed as the minimum value of contrast among all background prototypes

S false(R_{i} false) = \underset{j}{truemin} C_{i j} false(R_{i}, R_{j}^{B} false)

where each image pixel belonging to R i is assigned with a saliency value S ( R i ) and the final saliency map is normalised to a fixed range [0, 1].…”

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Owing to the limited computational resources (less powerful processor and limited memory) of electronic equipments, the designed saliency models are expected to be easy to implement, while maintaining accurate and robust detection results. Therefore, considerable effort has been devoted to detecting salient regions over the past few years [1][2][3][4][5][6][7].Existing saliency models could be categorised into two classes: top-down [8-10] and bottom-up [6,[11][12][13]. Top-down methods employ high-level cues (e.g.…”

mentioning

confidence: 99%

“…Graph-based saliency models [6,12] are suggested to predict saliency following the principle of Markov random work theory. Some researchers attempted to detect irregularities such as visual saliency in the frequency domain [1,13]. These saliency models, however, have limited capacity in complex scenes, where the salient and background regions are often heterogeneous.…”

mentioning

confidence: 99%

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Object‐based attention: saliency detection using contrast via background prototypes

Zhou

2014

Electron. lett.

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An object-based attention model to predict visual saliency using contrast against the 'background prototypes' is presented. The proposed model automatically identifies a series of regions far away from the image centre as background prototypes. The visual saliency is then calculated using the colour contrast against these background prototypes. Promising experimental results demonstrate the effectiveness of the proposed model in terms of detection accuracy and implementation efficiency.Introduction: The human attention system (HAS) is able to quickly detect the most interesting regions in a given scene. An important and open issue in computer vision is to simulate the HAS in humanmachine interactions, which enables the hardware systems to automatically focus on and capture interesting target objects. Owing to the limited computational resources (less powerful processor and limited memory) of electronic equipments, the designed saliency models are expected to be easy to implement, while maintaining accurate and robust detection results. Therefore, considerable effort has been devoted to detecting salient regions over the past few years [1][2][3][4][5][6][7].Existing saliency models could be categorised into two classes: top-down [8-10] and bottom-up [6,[11][12][13]. Top-down methods employ high-level cues (e.g. face); however, it is hardly generalised since their learning process often needs numerous computing resources. On the contrary, bottom-up approaches mainly estimate the foreground saliency based on simple low-level image features (e.g. luminance, colour and orientation), thus they are more convenient to apply to the practical scenarios of human-machine interactions. As a pioneer work, Itti et al.[11] introduced a biologically inspired saliency model based on the centre-surround operation. Graph-based saliency models [6,12] are suggested to predict saliency following the principle of Markov random work theory. Some researchers attempted to detect irregularities such as visual saliency in the frequency domain [1,13]. These saliency models, however, have limited capacity in complex scenes, where the salient and background regions are often heterogeneous.Another instance of biological evidence shows that human visual attention is often attracted to the image centre [14], since the background tends to be located in the image boundary. Inspired by this biological fact, we propose a bottom-up saliency model relying on the contrast against 'background prototypes'. Here, 'background prototypes' mean the superpixels are located far away from the image centre, even in the boundary of an image. Another important aspect of saliency modelling is how to computationally measure visual saliency. The traditional models often estimate saliency using the centre-surround contrast [11] or the global contrast against the entire scene [10, 15]. On the contrary, we resort to calculating colour-based contrast with respect to the background prototypes. The experimental results show the effectiveness of our model in terms of more rob...

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S false(R_{i} false) = \underset{j}{truemin} C_{i j} false(R_{i}, R_{j}^{B} false)

where each image pixel belonging to R i is assigned with a saliency value S ( R i ) and the final saliency map is normalised to a fixed range [0, 1].…”

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%