目的 随着互联网技术的发展，信息的数量呈几何级数增长。信息改变着人类的传统生活方式，它可以给人们的娱乐，教育，商业活动提供便利。但是另一方面，需要处理的信息数量大大超过了计算机的处理能力，因此，如何使计算机能像人眼一样可以自动在大量信息中选择重要信息进行加工就显得十分重要。图像的视觉显著性信息能够反映图像中不同区域对人视觉系统刺激的程度。可靠的显著性方法可以从大量的信息中自动预测预测和挖掘重要的视觉信息，这些信息可以为图像分割、图像检索等应用提供有价值的线索。目前，显著性检测算法的鲁棒性和实时性是研究的热点。本文提出一种基于拉普拉斯支持向量机（LapSVM）的快速显著性检测方法。方法 采用简单线性迭代聚类算法SLIC（simple linear iterative clustering）将原始图像首先分成若干个超像素块，并用它代替图像像素参与计算，可以减少算法所需的计算量。利用超像素之间相似性构建图Laplacian。分析每个图像块的边缘特性定义粗糙标识样本，并利用一种快速LapSVM进行分类。LapSVM是一种基于流形正则化的半监督分类方法。通过引入提前停止机制来加速LapSVM的训练。这样可以快速地计算出一个近似结果。计算的复杂性由原来的O（n³）降到了O（kn²），其中n是未标识样本和标识样本的数量。k是经验评价值，它远远小于n。通过分析得到的分类结果，提取出更准确的背景和目标样本作为新的标识样本再次进行LapSVM分类。最后，利用能量函数对分类结果进行优化得到最终的显著性检测结果。结果 ASD数据库是MSRA数据库的子集，包含1 000幅图片，被广泛用于各种显著性检测算法的实验中。本文算法在ASD图像数据库上与7种流行的图像显著性检测算法进行对比实验。本文算法不仅在准确率和召回率上保持了与其他算法相当的鲁棒性，平均绝对误差达到4%左右，同时算法的平均运行时间缩短为0.03 s左右。结论 提出一种基于LapSVM的快速图像显著性检测算法，通过区域边缘特征和分类结果分析，实现图像中背景和目标样本的准确检测。实验结果表明，本文算法具有良好的鲁棒性，显著地提高了算法的实时性。因此，与其他算法相比本文算法更适用于检测跟踪等实时性要求较高的场合。该方法可以在较短的时间内，以更好地准确率水平提取显著性区域。

Objective With the development of Internet technologies,the amount of information has grown exponentially.The information can change the traditional ways of people's lifestyle.They can bring great convenience in daily amusement,education,and commerce,but they also lead to many new challenges on the existing processing technologies.On one hand,people need to handle the amount of information,which greatly exceeds the processing capability of computers.Thus,allocating the limited computational resource to the important visual information is important.On the other hand,people hope computers can simulate the functions of human eyes,which can effortlessly select a small amount of important information for further complex processing.Visual saliency of images can reflect the degree of stimulation of the human visual system to different regions.The reliable saliency methods can automatically predict,locate,and mine the important visual information.Thus,it can help computers in effective selection of important information from the massive visual data,which is suitable for image segmentation and image retrieval.At present,the robustness and real-time performance of the algorithm have been a very active research area.In this paper,we present a rapid saliency detection method based on Laplacian Support Vector Machines(LapSVM).The proposed saliency method can extract the salient regions in image within a relatively short period of times and reach a better accuracy.Method First,we segment the source image into many regular regions using the Simple Linear Iterative Clustering(SLIC) algorithm.The SLIC algorithm is a simple and efficient method to decompose an image in visually homogeneous regions.These regions are called superpixels,which provide a convenient way to calculate local features.They can reduce the complexity of image processing by obtaining the redundancy of images.In this paper,we use superpixels instead of image pixels to participate in the calculation of the algorithm,thus reducing the amount of computation required.Then,we construct the graph using the similarity between character of regions.Second,we define the rough-labelled samples using the boundary feature of image regions,and classify using LapSVM algorithm.LapSVM has shown the state-of-the-art performance in semi-supervised classification.Following the manifold regularization approach,the LapSVM used is trained in the primal.We speed-up the training by using an early stopping strategy based on the prediction on unlabeled data or,if available,on labeled validation examples.This allows the algorithm to quickly compute approximate results with roughly the same classification accuracy as the optimal ones,considerably reducing the training time.The computational complexity of the training algorithm is reduced from O(n³) to O(kn²),where n is the combined number of labeled and unlabeled samples and k is empirically evaluated to be significantly smaller than n.Thus,the LapSVM trained in the primal is the primary tool of the proposed saliency detection algorithm.Third,we extract the more robust labelled samples based on analysis of the result of classification.Then,the classification must be done again based on LapSVM.The classification result of every superpixel is the probability of the category to be owned,and we define the probability as the saliency value of the superpixel.Finally,we get the saliency map using the energy function to optimize the classification result.The saliency map is a gray image and its intensity is between 0 and 1.We can use the ground truth to verify the accuracy of results.Result Compared with seven other well-known saliency detection algorithms on ASD dataset.The ASD dataset is the subdataset of the MSRA dataset,and it contains 1 000 images.It is widely used in the saliency detection experiments of many algorithms.Experiments show that the proposed algorithm exhibits impressive performance with real feature and it maintains also good robustness.The running time of the proposed algorithm is shortened to about 0.03 s and Mean Absolute Error(MAE) is about 4%.Conclusion We propose a novel rapid saliency detection method based on LapSVM.Using the boundary feature of regions and analysis of classification,we get the more accurate background and foreground samples.Experimental results prove that the proposed algorithm maintains the robustness compared with the latest algorithms,and greatly reduces the running time.Therefore,compared with other algorithms,the proposed algorithm is more suitable for real-time application,such as detection and tracking.