Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Common approaches to human action recognition from im- ages rely on local descriptors for classication. Typically, these descrip- tors are computed in the vicinity of key points which either result from running a key point detector or from dense or random sampling of pixel coordinates. Such key points are not a-priori related to human activi- ties and thus of limited information with regard to action recognition. ,  propose to identify action-specic key points in images using information available from videos. Our approach does not require manual segmentation or templates but applies non-negative matrix fac- torization to optical ow elds extracted from videos. The resulting basis ows are found to to be indicative of action specic image regions and therefore allow for an informed sampling of key points.  also present a generative model that allows for characterizing joint distributions of regions of interest and a human actions. In practical experiments,  determine correspondences beten regions of interest that re auto- matically learned from videos and manually annotated locations of hu- man body parts available from independent benchmark image data sets.  observe high correlations beten learned interest regions and body parts most relevant for dierent actions.
['Abdalrahman Eweiwi', 'Shahzad Cheema', 'Christian Bauckhage']
I am writing a research paper on a idea Learning Spatial Interest Regions from Videos to Inform Action Recognition in Still Images
[{"from": "human", "value": "I am writing a research paper on a idea Learning Spatial Interest Regions from Videos to Inform Action Recognition in Still Images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Common approaches to human action recognition from im- ages rely on local descriptors for classication. Typically, these descrip- tors are computed in the vicinity of key points which either result from running a key point detector or from dense or random sampling of pixel coordinates. Such key points are not a-priori related to human activi- ties and thus of limited information with regard to action recognition. ,  propose to identify action-specic key points in images using information available from videos. Our approach does not require manual segmentation or templates but applies non-negative matrix fac- torization to optical ow elds extracted from videos. The resulting basis ows are found to to be indicative of action specic image regions and therefore allow for an informed sampling of key points.  also present a generative model that allows for characterizing joint distributions of regions of interest and a human actions. In practical experiments,  determine correspondences beten regions of interest that re auto- matically learned from videos and manually annotated locations of hu- man body parts available from independent benchmark image data sets.  observe high correlations beten learned interest regions and body parts most relevant for dierent actions."}]