Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Organizing images into semantic categories can be extremely useful for content-based image retrieval and image annotation. Grouping images into semantic classes is a difficult problem, hover. Image classification attempts to solve this hard problem by using low-level image features. ,  propose amethod for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality.  use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages.
['Jing Huang', 'S. Ravi Kumar', 'Ramin Zabih']
I am writing a research paper on a idea Automatic hierarchical color image classification
[{"from": "human", "value": "I am writing a research paper on a idea Automatic hierarchical color 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: Organizing images into semantic categories can be extremely useful for content-based image retrieval and image annotation. Grouping images into semantic classes is a difficult problem, hover. Image classification attempts to solve this hard problem by using low-level image features. ,  propose amethod for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality.  use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages."}]