Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A key ingredient in the design of visual object classification systems is the identification of relevant class specific aspects while being robust to intra-class variations. While this is a necessity in order to generalize beyond a given set of training images, it is also a very difficult problem due to the high variability of visual appearance within each class. In the last years substantial performance gains on challenging benchmark datasets have been reported in the literature. This progress can be attributed to two developments: the design of highly discriminative and robust image features and the combination of multiple complementary features based on different aspects such as shape, color or texture.   study several models that aim at learning the correct ighting of different features from training data. These include multiple kernel learning as ll as simple baseline methods. Furthermore  derive ensemble methods inspired by Boosting which are easily extendable to several multiclass setting. All methods are thoroughly evaluated on object classification datasets using a multitude of feature descriptors. The key results are that even very simple baseline methods, that are orders of magnitude faster than learning techniques are highly competitive with multiple kernel learning. Furthermore the Boosting type methods are found to produce consistently better results in all experiments.  provide insight of when combination methods can be expected to work and how the benefit of complementary features can be exploited most efficiently.
['Peter V. Gehler', 'Sebastian Nowozin']
I am writing a research paper on a idea On feature combination for multiclass object classification
[{"from": "human", "value": "I am writing a research paper on a idea On feature combination for multiclass object classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A key ingredient in the design of visual object classification systems is the identification of relevant class specific aspects while being robust to intra-class variations. While this is a necessity in order to generalize beyond a given set of training images, it is also a very difficult problem due to the high variability of visual appearance within each class. In the last years substantial performance gains on challenging benchmark datasets have been reported in the literature. This progress can be attributed to two developments: the design of highly discriminative and robust image features and the combination of multiple complementary features based on different aspects such as shape, color or texture.   study several models that aim at learning the correct ighting of different features from training data. These include multiple kernel learning as ll as simple baseline methods. Furthermore  derive ensemble methods inspired by Boosting which are easily extendable to several multiclass setting. All methods are thoroughly evaluated on object classification datasets using a multitude of feature descriptors. The key results are that even very simple baseline methods, that are orders of magnitude faster than learning techniques are highly competitive with multiple kernel learning. Furthermore the Boosting type methods are found to produce consistently better results in all experiments.  provide insight of when combination methods can be expected to work and how the benefit of complementary features can be exploited most efficiently."}]