Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Histograms are used in almost every aspect of image processing and computer vision, from visual descriptors to image representations. Histogram intersection kernel (HIK) and support vector machine (SVM) classifiers are shown to be very effective in dealing with histograms.  presents contributions concerning HIK SVM for image classification. First,  propose intersection coordinate descent (ICD), a deterministic and scalable HIK SVM solver. ICD is much faster than, and has similar accuracies to, general purpose SVM solvers and other fast HIK SVM training methods.  also extend ICD to the efficient training of a broader family of kernels. Second,  show an important empirical observation that ICD is not sensitive to the  C  parameter in SVM, and  provide some theoretical analyses to explain this observation. ICD achieves high accuracies in many problems, using its default parameters. This is an attractive property for practitioners, because many image processing tasks are too large to choose SVM parameters using cross-validation.
['Jianxin Wu']
I am writing a research paper on a idea Efficient HIK SVM Learning for Image Classification
[{"from": "human", "value": "I am writing a research paper on a idea Efficient HIK SVM Learning for Image Classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Histograms are used in almost every aspect of image processing and computer vision, from visual descriptors to image representations. Histogram intersection kernel (HIK) and support vector machine (SVM) classifiers are shown to be very effective in dealing with histograms.  presents contributions concerning HIK SVM for image classification. First,  propose intersection coordinate descent (ICD), a deterministic and scalable HIK SVM solver. ICD is much faster than, and has similar accuracies to, general purpose SVM solvers and other fast HIK SVM training methods.  also extend ICD to the efficient training of a broader family of kernels. Second,  show an important empirical observation that ICD is not sensitive to the  C  parameter in SVM, and  provide some theoretical analyses to explain this observation. ICD achieves high accuracies in many problems, using its default parameters. This is an attractive property for practitioners, because many image processing tasks are too large to choose SVM parameters using cross-validation."}]