Object Detection Based On Visual Saliency And Unsupervised Learning

Human visual neural system can subjectively process the natural scene,because of the limitation of computing and high timeliness requirements,the process will be simplified compressed:the optic nerve will choose the scene which most attract its attention to understand,this is called selective attention mechanism of human vision.In turn,starting from the images which can reflect the natural scene,the property that attract the optic nerve,known as visual saliency.Therefore,simulating human visual neural system,can construct image saliency target detection models.Nowadays,many domestic and foreign research groups are working on this job.This article mainly finished the following work from the perspective of image contrast information:Optimizing the existing saliency object detection methods based on the information of the global contrast.It's known that reducing the amount of color can greatly accelerate the efficiency of the algorithm,we have put forward,in the process of color quantization,there are some defects existing algorithm: the process of color quantization is aimed directly at the three channels of RGB color model,not from the perspective of the overall image statistics to quantify the color,and this is likely to cause the distortion of images to a certain extent after the quantization.After know the problem,we put forward that using octree algorithm to represent colors is a good method to count the color frequency and it can construct a palette,which can complete the work of color quantization.Then we use histogram acceleration and denoising smooth operation to get the final saliency map.The saliency map generated by our method is better than the original one in in the aspect of effect.At the same time,it spends less time.Compared with several classical algorithms based on different principles,the method in this paper has better visual effect and consume less time.We complete the segmentation of saliency maps using fuzzy c-means clustering algorithm.The fuzzy c-means clustering(FCM)algorithm is a kind of soft clustering algorithm based on fuzzy theory,applying the algorithm to image segmentation can obtain better results than traditional hard clustering algorithm.We proposed that combine the results of saliency detection with segmentation process based on FCM algorithm,can get better processing effect through the proof of the experiment.The proposed approach can be competent at the work of saliency target detection,and has outstanding performance in the aspect of visual effect and time.The integrated application of saliency detection and FCM image segmentation algorithm can be more competent.The work of this paper can be applied to the work of image content perception and image retrieval.