Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ranking hypothesis sets is a porful concept for efficient object detection. In this work,  propose a branch&rank scheme that detects objects with often less than 100 ranking operations. This efficiency enables the use of strong and also costly classifiers like non-linear SVMs with RBF- $$\chi ^2$$ ? 2 kernels.  thereby relieve an inherent limitation of branch&bound methods as bounds are often not tight enough to be effective in practice. Our approach features three key components: a ranking function that operates on sets of hypotheses and a grouping of these into different tasks. Detection efficiency results from adaptively sub-dividing the object search space into decreasingly smaller sets. This is inherited from branch&bound, while the ranking function supersedes a tight bound which is often unavailable (except for rather limited function classes). The grouping makes the system effective: it separates image classification from object recognition, yet combines them in a single formulation, phrased as a structured SVM problem. A novel aspect of branch&rank is that a better ranking function is expected to decrease the number of classifier calls during detection.  use the VOC'07 dataset to demonstrate the algorithmic properties of branch&rank.
['Alain D. Lehmann', 'Peter V. Gehler', 'Luc J. Van Gool']
I am writing a research paper on a idea Branch&Rank for Efficient Object Detection
[{"from": "human", "value": "I am writing a research paper on a idea Branch&Rank for Efficient Object Detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ranking hypothesis sets is a porful concept for efficient object detection. In this work,  propose a branch&rank scheme that detects objects with often less than 100 ranking operations. This efficiency enables the use of strong and also costly classifiers like non-linear SVMs with RBF- $$\\chi ^2$$ ? 2 kernels.  thereby relieve an inherent limitation of branch&bound methods as bounds are often not tight enough to be effective in practice. Our approach features three key components: a ranking function that operates on sets of hypotheses and a grouping of these into different tasks. Detection efficiency results from adaptively sub-dividing the object search space into decreasingly smaller sets. This is inherited from branch&bound, while the ranking function supersedes a tight bound which is often unavailable (except for rather limited function classes). The grouping makes the system effective: it separates image classification from object recognition, yet combines them in a single formulation, phrased as a structured SVM problem. A novel aspect of branch&rank is that a better ranking function is expected to decrease the number of classifier calls during detection.  use the VOC'07 dataset to demonstrate the algorithmic properties of branch&rank."}]