Static and space-time visual saliency detection by self-resemblance

Seo, Hae Jong; Milanfar, Peyman

doi:10.1167/9.12.15

Cited by 575 publications

(406 citation statements)

References 36 publications

Supporting

Mentioning

396

Contrasting

Unclassified

Order By: Relevance

“…Salient features are extracted from said descriptors and compared against analogous features from the target image. The detection framework of LARK can also be useful for solving the bottom-up saliency detection [18]. Therefore, the schematic diagram of our framework modified as shown in Fig.…”

Section: An Extended Algorithm Implementation For Salient Object Detementioning

confidence: 99%

A Visual Attention Based Object Detection Model beyond Top-Down and Bottom-up Mechanism

Zhang

Liu

2017

ITM Web Conf.

View full text Add to dashboard Cite

Traditional saliency-based attention theory supposed that bottom-up and top-down factors combine to direct attentional behavior. This dichotomy fails to explain a growing number of cases in which neither bottom-up nor top-down can account for strong selection biases. Thus, the top-down versus bottom-up dichotomy is an inadequate taxonomy of attentional control. In our previous study, we presented a general computational framework for detecting task-oriented salient objects in images beyond top-down and bottom-up mechanism. It possesses three parts: selection history, current goal and physical salience. Selection history is integrated with current goal and physical salience to compose an integrative framework. In this extended version, our ameliorated model is applied to face and car detection and simulates taskdependent reasonable eye trajectories (visual scan paths). Experimental results demonstrate that selection history (reward) is shown to influence saccade trajectories. Our findings support the idea that attention and gaze can be directed voluntarily to regions of interest (by selection history and current goal) and can be captured by local features of an object that stand out from the background (by physical salience). IntroductionVisual search plays a key role in our everyday activities; the visual system pays attention to the salient objects for efficient search. Visual saliency plays important roles in natural vision in that saliency can direct eye movements, deploy attention, and facilitate tasks like object detection and scene understanding. Attention determines which among multiple competing stimuli are represented in the brain. . Saliency models have been developed for eye fixation prediction and salient object detection. The former focuses on identifying a few fixation locations on natural images, which is important for understanding human attention. The latter, also called salient object segmentation, is used to accurately detect where the salient object should be, which is useful for many high-level vision tasks [24] [6]. Recently, the theoretical dichotomy of attentional control between top-down and bottom-up is challenged. The dichotomy fails to explain a growing number of cases in which neither bottom-up nor top-down can account for strong selection biases [4]. Thus, the top-down versus bottom-up dichotomy is an inadequate taxonomy of attentional control. A wealth of research has demonstrated that reward information provides a third source of input to the attention system. When a large reward is received, attention is strongly primed to select the rewarded target [12] [10].Awh et al. [4] proposed selection history (including two classes of 'history' effects, i.e., selection and reward history) as a third category of control by explicitly distinguishing current goals from selection history effects. A 'priority map' which they still highlighted integrates three distinct categories of selection bias: the observer's current selection goals, selection history, and physical

show abstract

Section: An Extended Algorithm Implementation For Salient Object Detementioning

confidence: 99%

A Visual Attention Based Object Detection Model beyond Top-Down and Bottom-up Mechanism

Zhang

Liu

2017

ITM Web Conf.

View full text Add to dashboard Cite

show abstract

“…However, it remains unclear how this strategy can act on the performance in other classification framework. In contrast with [39], where only one saliency detection method (context-aware) is evaluated, we investigate fifteen kinds of typical methods, which are AC [53], attention by information maximization (AIM) [54], context-aware (CA) [52], context-based (CB) [55], discriminative regional feature integration (DRFI) [56], frequency-tuned (FT) [57], graph-based visual saliency (GBVS) [58], IM [59], low rank matrix recovery (LRR) [60], maximum symmetric surround (MSS) [61], rarity-based saliency (RARE) [62], salient segmentation (SEG) [63], self-resemblance (SeR) [64], spectral residual (SR) [65] and saliency using natural statistics (SUN) [66], and present detailed comparative experiments for them. We do not review these methods due to the limited space, and details of each method can be found in its corresponding paper.…”

Section: Feature Coding Based On the Saliency Mapmentioning

confidence: 99%

Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

Xia

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

Scene classification of high-resolution remote sensing (HRRS) imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW) model and its variants, can achieve acceptable performance, these approaches strongly rely on the extraction of local features and the complicated coding strategy, which are usually time consuming and demand much expert effort. In this paper, we propose a fast binary coding (FBC) method, to effectively generate efficient discriminative scene representations of HRRS images. The main idea is inspired by the unsupervised feature learning technique and the binary feature descriptions. More precisely, equipped with the unsupervised feature learning technique, we first learn a set of optimal "filters" from large quantities of randomly-sampled image patches and then obtain feature maps by convolving the image scene with the learned filters. After binarizing the feature maps, we perform a simple hashing step to convert the binary-valued feature map to the integer-valued feature map. Finally, statistical histograms computed on the integer-valued feature map are used as global feature representations of the scenes of HRRS images, similar to the conventional BOW model. The analysis of the algorithm complexity and experiments on HRRS image datasets demonstrate that, in contrast with existing scene classification approaches, the proposed FBC has much faster computational speed and achieves comparable classification performance. In addition, we also propose two extensions to FBC, i.e., the spatial co-occurrence matrix and different visual saliency maps, for further improving its final classification accuracy.

show abstract

“…The model consists of two parts [8] [9]. First, they propose to use local regression kernels as features.…”

Section: Sdsr: Saliency Detection By Self-resemblance (2009)mentioning

confidence: 99%

A CBIR-based evaluation framework for visual attention models

Awad

Mancaş

Riche

et al. 2015

2015 23rd European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

The computational models of visual attention, originally proposed as cognitive models of human attention, nowadays are being used as front-ends to numerous vision systems like automatic object recognition. These systems are generally evaluated against eye tracking data or manually segmented salient objects in images. We previously showed that this comparison can lead to different rankings depending on which of the two ground truths is used. These findings suggest that the saliency models ranking might be different for each application and the use of eye-tracking rankings to choose a model for a given application is not optimal. Therefore, in this paper, we propose a new saliency evaluation framework optimized for object recognition. This paper aims to answer the question: 1) Is the application-driven saliency models rankings consistent with classical ground truth like eye-tracking? 2) If not, which saliency models one should use for the precise CBIR applications?

show abstract

Static and space-time visual saliency detection by self-resemblance

Cited by 575 publications

References 36 publications

A Visual Attention Based Object Detection Model beyond Top-Down and Bottom-up Mechanism

A Visual Attention Based Object Detection Model beyond Top-Down and Bottom-up Mechanism

Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

A CBIR-based evaluation framework for visual attention models

Contact Info

Product

Resources

About