Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A variety of flexible models have been proposed to detect objects in challenging real world scenes. Motivated by some of the most successful techniques,  propose a hierarchical multi-feature representation and automatically learn flexible hierarchical object models for a wide variety of object classes. To that end  not only rely on automatic selection of relevant individual features, but go beyond previous work by automatically selecting and modeling complex, long-range feature couplings within this model. To achieve this generality and flexibility our work combines structure learning in conditional random fields and discriminative parameter learning of classifiers using hierarchical features.  adopt an efficient gradient based heuristic for model selection and carry it forward to discriminative, multidimensional selection of features and their couplings for improved detection performance. Experimentally  consistently outperform the currently leading method on all 20 classes of the PASCAL VOC 2007 challenge and achieve the best published results on 16 of 20 classes.
['Paul Schnitzspan', 'Mario Fritz', 'Stefan Roth', 'Bernt Schiele']
I am writing a research paper on a idea Discriminative structure learning of hierarchical representations for object detection
[{"from": "human", "value": "I am writing a research paper on a idea Discriminative structure learning of hierarchical representations for 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: A variety of flexible models have been proposed to detect objects in challenging real world scenes. Motivated by some of the most successful techniques,  propose a hierarchical multi-feature representation and automatically learn flexible hierarchical object models for a wide variety of object classes. To that end  not only rely on automatic selection of relevant individual features, but go beyond previous work by automatically selecting and modeling complex, long-range feature couplings within this model. To achieve this generality and flexibility our work combines structure learning in conditional random fields and discriminative parameter learning of classifiers using hierarchical features.  adopt an efficient gradient based heuristic for model selection and carry it forward to discriminative, multidimensional selection of features and their couplings for improved detection performance. Experimentally  consistently outperform the currently leading method on all 20 classes of the PASCAL VOC 2007 challenge and achieve the best published results on 16 of 20 classes."}]