Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  investigate how to establish the relationship beten semantic concepts based on the large-scale real-world click data from image commercial engine, which is a challenging topic because the click data suffers from the noise such as typos, the same concept with different queries, etc.  first define five specific relationships beten concepts.  then extract some concept relationship features in textual and visual domain to train the concept relationship models. The relationship of each pair of concepts will thus be classified into one of the five special relationships.  study the efficacy of the conceptual relationships by applying them to augment imperfect image tags, i.e., improve representative por.  further employ a sophisticated hashing approach to transform augmented image tags into binary codes, which are subsequently used for content-based image retrieval task. Experimental results on NUS-WIDE dataset demonstrate the superiority of our proposed approach as compared to state-of-the-art methods.
['Richang Hong', 'Yang Yang', 'Meng Wang', 'Xian-Sheng Hua']
I am writing a research paper on a idea Learning Visual Semantic Relationships for Efficient Visual Retrieval
[{"from": "human", "value": "I am writing a research paper on a idea Learning Visual Semantic Relationships for Efficient Visual Retrieval"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  investigate how to establish the relationship beten semantic concepts based on the large-scale real-world click data from image commercial engine, which is a challenging topic because the click data suffers from the noise such as typos, the same concept with different queries, etc.  first define five specific relationships beten concepts.  then extract some concept relationship features in textual and visual domain to train the concept relationship models. The relationship of each pair of concepts will thus be classified into one of the five special relationships.  study the efficacy of the conceptual relationships by applying them to augment imperfect image tags, i.e., improve representative por.  further employ a sophisticated hashing approach to transform augmented image tags into binary codes, which are subsequently used for content-based image retrieval task. Experimental results on NUS-WIDE dataset demonstrate the superiority of our proposed approach as compared to state-of-the-art methods."}]