Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel recognition approach in which the component-adaptive color co-occurrence matrices (CACCMs) are designed to characterize color and texture cues in the images, while histogram intersection kernel support vector machines (HIKSVMs) are generalized to the version compatible to color co-occurrence matrix (CCM), called CCM intersection kernel support vector machines (CIKSVMs). An ensemble learning framework is proposed for synchronously training the optimal marginal CIKSVMs and corresponding CACCMs’ extractors. This learning architecture is applicable to an arbitrary color space employed for image coding, while  pay utmost attention to a perceptual uniform color space for the prominent potential in image proprieties’ display. For the formulation of recognition algorithm, the set of multi-channel CACCMs (CAMCMs) of per sample is utilized to get a balance beten discriminative por and computational efficiency, while multiple marginal CIKSVMs are combined by ighted majority voting. The effectiveness of our approach is validated by promising results obtained from four experimental datasets.
['Jian Zou', 'Gui-Fu Lu', 'Yue Zhang', 'Chuancai Liu']
I am writing a research paper on a idea Generalizing intersection kernel support vector machines for color texture based recognition
[{"from": "human", "value": "I am writing a research paper on a idea Generalizing intersection kernel support vector machines for color texture based recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel recognition approach in which the component-adaptive color co-occurrence matrices (CACCMs) are designed to characterize color and texture cues in the images, while histogram intersection kernel support vector machines (HIKSVMs) are generalized to the version compatible to color co-occurrence matrix (CCM), called CCM intersection kernel support vector machines (CIKSVMs). An ensemble learning framework is proposed for synchronously training the optimal marginal CIKSVMs and corresponding CACCMs\u2019 extractors. This learning architecture is applicable to an arbitrary color space employed for image coding, while  pay utmost attention to a perceptual uniform color space for the prominent potential in image proprieties\u2019 display. For the formulation of recognition algorithm, the set of multi-channel CACCMs (CAMCMs) of per sample is utilized to get a balance beten discriminative por and computational efficiency, while multiple marginal CIKSVMs are combined by ighted majority voting. The effectiveness of our approach is validated by promising results obtained from four experimental datasets."}]