Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ranking is the central problem for many applications such as b search, recommendation systems, and visual comparison of images. , the multiple kernel learning framework is generalized for the learning to rank problem. This approach extends the existing learning to rank algorithms by considering multiple kernel learning and consequently improves their effectiveness. The proposed approach provides the convenience of fusing different features for describing the underlying data. As an application to our approach, the problem of visual image comparison is studied. Several visual features are used for describing the images and multiple kernel learning is adopted to find an optimal feature fusion. Experimental results on three challenging datasets show that our approach outperforms the state-of-the art and is significantly more efficient in runtime.
['Amr Sharaf', 'Mohamed E. Hussein', 'Mohamed A. Ismail']
I am writing a research paper on a idea Visual Comparison of Images Using Multiple Kernel Learning for Ranking
[{"from": "human", "value": "I am writing a research paper on a idea Visual Comparison of Images Using Multiple Kernel Learning for Ranking"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ranking is the central problem for many applications such as b search, recommendation systems, and visual comparison of images. , the multiple kernel learning framework is generalized for the learning to rank problem. This approach extends the existing learning to rank algorithms by considering multiple kernel learning and consequently improves their effectiveness. The proposed approach provides the convenience of fusing different features for describing the underlying data. As an application to our approach, the problem of visual image comparison is studied. Several visual features are used for describing the images and multiple kernel learning is adopted to find an optimal feature fusion. Experimental results on three challenging datasets show that our approach outperforms the state-of-the art and is significantly more efficient in runtime."}]