Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Color names are required in real-world applications such as image retrieval and image annotation. Traditionally, they are learned from a collection of labeled color chips. These color chips are labeled with color names within a ll-defined experimental setup by human test subjects. Hover, naming colors in real-world images differs significantly from this experimental setting. ,  investigate how color names learned from color chips compare to color names learned from real-world images. To avoid hand labeling real-world images with color names,  use Google image to collect a data set. Due to the limitations of Google image, this data set contains a substantial quantity of wrongly labeled data.  propose several variants of the PLSA model to learn color names from this noisy data. Experimental results show that color names learned from real-world images significantly outperform color names learned from labeled color chips for both image retrieval and image annotation.
['J. van de Weijer', 'C. Schmid', 'Jakob J. Verbeek', 'Diane Larlus']
I am writing a research paper on a idea Learning Color Names for Real-World Applications
[{"from": "human", "value": "I am writing a research paper on a idea Learning Color Names for Real-World Applications"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Color names are required in real-world applications such as image retrieval and image annotation. Traditionally, they are learned from a collection of labeled color chips. These color chips are labeled with color names within a ll-defined experimental setup by human test subjects. Hover, naming colors in real-world images differs significantly from this experimental setting. ,  investigate how color names learned from color chips compare to color names learned from real-world images. To avoid hand labeling real-world images with color names,  use Google image to collect a data set. Due to the limitations of Google image, this data set contains a substantial quantity of wrongly labeled data.  propose several variants of the PLSA model to learn color names from this noisy data. Experimental results show that color names learned from real-world images significantly outperform color names learned from labeled color chips for both image retrieval and image annotation."}]