影象顯著性是影象中重要的視覺特徵，體現了人眼對影象的某些區域的重視程度。自從1998年Itti的工作以來，產生了大量的顯著性對映方法，影象顯著性也廣泛應用於影象壓縮、編碼、影象邊緣和區域加強、顯著性目標分割和提取等.

      對於一幅影象來說，使用者只對影象中的部分割槽域感興趣，這部分感興趣的區域代表了使用者的查詢意圖，而多數剩餘的不感興趣區域則與使用者查詢意圖無關.顯著區域是影象中最能引起使用者興趣、最能表現影象內容的區域.事實上，顯著區域的選擇是非常主觀的，由於使用者任務和知識背景的不同，對於同一幅影象，不同的使用者可能會選擇不同的區域作為顯著區域. 常用的方法是利用人的注意力機制為基礎計算影象的顯著度.認知心理學的研究表明，影象中有些區域能顯著的吸引人的注意，這些區域含有較大的資訊量.認知科學家已經提出了許多數學模型來模擬人的注意力機制.由於利用了影象認知過程中的一般規律，這樣提取的顯著區域比較符合人的主觀評價.
      影象顯著性分析應用廣泛，主要包括：基於顯著性區域的自適應影象壓縮和編碼，基於顯著性度量的影象邊緣或區域加強，基於顯著性分析的目標分割或提取等。
      目前出現越來越多的顯著性分析演算法，有些人將這些演算法進行了一定的分類。[8]中提到將目前顯著性分析的演算法分成三類：第一類是基於低層視覺特徵的顯著性分析演算法

，其代表性演算法是[1]中提出的演算法（Itti演算法），這是一種模擬生物體視覺注意機制的選擇性注意演算法，比較適合處理自然影象。第二類是不基於任何生物視覺原理的純數學計算方法，這類主要有[7]中提到的一種全解析度的演算法（AC演算法）和[9]中提出的基於空間頻域分析的剩餘譜演算法(SR演算法)。第三類是將前兩種進行融合的方法，其代表性演算法是[6]中提出基於圖論的演算法（GBVS演算法），這種演算法在特徵提取的過程中類似Itti演算法去模擬視覺原理，但在顯著圖生成的過程引入馬爾可夫連，用純數學計算的來得到顯著值。而在[10]中又將顯著性分析演算法分成以下三類：考慮區域性特徵的，例如Itti演算法和GBVS演算法；考慮整體性的，例如SR演算法和[8]中提出的演算法（IG演算法）；區域性與整體結合的,例如[10]，[11]中提出的演算法。

        常用的顯著性方法介紹:

	"A Model of Saliency-Based Visual Attention for Rapid Scene Analysis"       Abstract—A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail.       Index Terms—Visual attention, scene analysis, feature extraction, target detection, visual search.
	"Graph-Based Visual Saliency"       Abstract—A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is proposed. It consists of two steps: rst forming activation maps on certain feature channels, and then normalizing them in a way which highlights conspicuity and admits combination with other maps. The model is simple, and biologically plausible insofar as it is naturally parallelized. This model powerfully predicts human xations on 749 variations of 108 natural images, achieving 98% of the ROC area of a human-based control, whereas the classical algorithms of Itti & Koch ([2], [3], [4]) achieve only 84%.
	"Salient Region Detection and Segmentation"       Abstract—Detection of salient image regions is useful for applications like image segmentation, adaptive compression, and region-based image retrieval. In this paper we present a novel method to determine salient regions in images using low-level features of luminance and color. The method is fast, easy to implement and generates high quality saliency maps of the same size and resolution as the input image.We demonstrate the use of the algorithm in the segmentation of semantically meaningful whole objects from digital images.       Key words: Salient regions, low-level features, segmentation
	"Frequency-tuned Salient Region Detection"       Abstract—Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. In this paper, we introduce a method for salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects. These boundaries are preserved by retaining substantially more frequency content from the original image than other existing techniques. Our method exploits features of color and luminance, is simple to implement, and is computationally efficient. We compare our algorithm to five state-of-the-art salient region detection methods with a frequency domain analysis, ground truth, and a salient object segmentation application. Our method outperforms the five algorithms both on the ground-truth evaluation and on the segmentation task by achieving both higher precision and better recall.
	"Saliency Detection: A Spectral Residual Approach"       Abstract—The ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, com- putational modeling of this basic intelligent behavior still remains a challenge. This paper presents a simple method for the visual saliency detection.       Our model is independent of features, categories, or other forms of prior knowledge of the objects. By analyz- ing the log-spectrum of an input image, we extract the spectral residual of an image in spectral domain, and propose a fast method to construct the corresponding saliency map in spatial domain.       We test this model on both natural pictures and artificial images such as psychological patterns. The result indicate fast and robust saliency detection of our method

參考文獻：
[1] L. Itti, C. Koch, & E. Niebur .A model of saliency based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11):1254-1259, 1998.
[2] L. Itti and C. Koch. Computational Model ling of Visual Attention. Nature Reviews Neuroscience, 2(3):194–203, 2001.
[3] C. Koch& S. Ullman . Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology, 4(4):219-227, 1985.
[4] L. Itti, & P. Baldi . Bayesian surprise attracts human attention. Advances in Neural Information Processing Systems, 19:547-554, 2005.
[5] C. Koch and T. Poggio. Predicting the Visual World: Silence is Golden. Nature Neuroscience, 2(1):9–10, 1999.
[6] J. Harel, C. Koch, &P. Perona. Graph-based visual saliency. Advances in Neural Information Processing Systems, 19:545-552, 2007.
[7] R. Achanta, F. Estrada, P. Wils, & S. S¨usstrunk. Salient region detection and segmentation. International Conference on Computer Vision Systems, 2008, pp.66-75.
[8] R. Achanta, S. Hemami ,F. Estrada,& S. S¨usstrunk Frequency-tuned salient region detection. IEEE International Conference on Computer Vision and Pattern Recognition, 2009, pp.1597-1604.
[9] X,Hou &L,Zhang. Saliency Detection: A spectral residual approach. IEEE Conference on Computer Vision and Pattern Recognition, 2007, pp.1-8.
[10] S. Goferman, L. Zelnik-Manor &A. Tal r. Context-aware saliency detection. Conference on Computer Vision and Pattern Recognition, 2010, pp. 9-16.
[11] T. Liu, J. Sun, N. Zheng, X. Tang, and H. Shum. Learning to detect a salient object. Conference on Computer Vision and Pattern Recognition, 2007, pp.1-8