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. 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() to O()

where is the combined number of labeled and unlabeled samples and is empirically evaluated to be significantly smaller than .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. 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%. 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.