We propose an automatic video segmentation method based on an optimized SaliencyCut equipped with information centroid(IC)detection according to level balance principle in physical theory.Unlike the existing methods,t...We propose an automatic video segmentation method based on an optimized SaliencyCut equipped with information centroid(IC)detection according to level balance principle in physical theory.Unlike the existing methods,the image information of another dimension is provided by the IC to enhance the video segmentation accuracy.Specifically,our IC is implemented based on the information-level balance principle in the image,and denoted as the information pivot by aggregating all the image information to a point.To effectively enhance the saliency value of the target object and suppress the background area,we also combine the color and the coordinate information of the image in calculating the local IC and the global IC in the image.Then saliency maps for all frames in the video are calculated based on the detected IC.By applying IC smoothing to enhance the optimized saliency detection,we can further correct the unsatisfied saliency maps,where sharp variations of colors or motions may exist in complex videos.Finally,we obtain the segmentation results based on IC-based saliency maps and optimized SaliencyCut.Our method is evaluated on the DAVIS dataset,consisting of different kinds of challenging videos.Comparisons with the state-of-the-art methods are also conducted to evaluate our method.Convincing visual results and statistical comparisons demonstrate its advantages and robustness for automatic video segmentation.展开更多
基金This work was supported in part by the Major Project of the New Generation of Artificial Intelligence of National Key Research and Development Project,Ministry of Science and Technology of China under Grant No.2018AAA0102900the National Natural Science Foundation of China under Grant Nos.61572328 and 61973221+1 种基金the Natural Science Foundation of Guangdong Province of China under Grant Nos.2018A030313381 and 2019A1515011165The Hong Kong Polytechnic University under Grant Nos.P0030419 and P0030929.
文摘We propose an automatic video segmentation method based on an optimized SaliencyCut equipped with information centroid(IC)detection according to level balance principle in physical theory.Unlike the existing methods,the image information of another dimension is provided by the IC to enhance the video segmentation accuracy.Specifically,our IC is implemented based on the information-level balance principle in the image,and denoted as the information pivot by aggregating all the image information to a point.To effectively enhance the saliency value of the target object and suppress the background area,we also combine the color and the coordinate information of the image in calculating the local IC and the global IC in the image.Then saliency maps for all frames in the video are calculated based on the detected IC.By applying IC smoothing to enhance the optimized saliency detection,we can further correct the unsatisfied saliency maps,where sharp variations of colors or motions may exist in complex videos.Finally,we obtain the segmentation results based on IC-based saliency maps and optimized SaliencyCut.Our method is evaluated on the DAVIS dataset,consisting of different kinds of challenging videos.Comparisons with the state-of-the-art methods are also conducted to evaluate our method.Convincing visual results and statistical comparisons demonstrate its advantages and robustness for automatic video segmentation.
