Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Graph-based video segmentation has demonstrated its influential impact from recent works. Hover, most of the existing approaches fail to make a semantic segmentation of the foreground objects, i.e. all the segmented objects are treated as one class. ,  propose an approach to semantically segment the multi-class foreground objects from a single video sequence. To achieve this,  firstly generate a set of proposals for each frame and score them based on motion and appearance features. With these scores, the similarities beten each proposal are measured. To tackle the vulnerability of the graph-based model, low-rank representation with l2,1-norm regularizer outlier detection is proposed to discover the intrinsic structure among proposals. With the "clean" graph representation, objects of different classes are more likely to be grouped into separated clusters. Two open public datasets MOViCS and ObMiC are used for evaluation under both intersection-over-union and F-measure metrics. The superior results compared with the state-of-the-arts demonstrate the effectiveness of the proposed method.
['Handong Zhao', 'Yun Fu']
I am writing a research paper on a idea Semantic single video segmentation with robust graph representation
[{"from": "human", "value": "I am writing a research paper on a idea Semantic single video segmentation with robust graph representation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Graph-based video segmentation has demonstrated its influential impact from recent works. Hover, most of the existing approaches fail to make a semantic segmentation of the foreground objects, i.e. all the segmented objects are treated as one class. ,  propose an approach to semantically segment the multi-class foreground objects from a single video sequence. To achieve this,  firstly generate a set of proposals for each frame and score them based on motion and appearance features. With these scores, the similarities beten each proposal are measured. To tackle the vulnerability of the graph-based model, low-rank representation with l2,1-norm regularizer outlier detection is proposed to discover the intrinsic structure among proposals. With the \"clean\" graph representation, objects of different classes are more likely to be grouped into separated clusters. Two open public datasets MOViCS and ObMiC are used for evaluation under both intersection-over-union and F-measure metrics. The superior results compared with the state-of-the-arts demonstrate the effectiveness of the proposed method."}]