Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  addresses the problem of fast similar image retrieval, especially for large-scale datasets with millions of images.  present a new framework which consists of two dependent algorithms. First, a new feature is proposed to represent images, which is dubbed compact feature based clustering (CFC). For each image,  first extract cluster centers of local features, and then calculate distribution histograms of local features and statistics of spatial information in each cluster to form compact features based clustering, replacing thousands of local features. It can reduce feature vectors of image representation and enhance the discriminative por of each feature. In addition, an efficient retrieval method is proposed, based on vocabulary tree through compact features based clustering. Extensive experiments on the Ukbench, Holidays, and ImageNet databases demonstrate that our method reduces the memory and computation overhead and improves the retrieval efficiency, while keeping approximate state-of-the-art accuracy.
['Yan Liang', 'Le Dong', 'Shanshan Xie', 'Na Lv', 'Zongyi Xu']
I am writing a research paper on a idea Compact feature based clustering for large-scale image retrieval
[{"from": "human", "value": "I am writing a research paper on a idea Compact feature based clustering for large-scale image retrieval"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  addresses the problem of fast similar image retrieval, especially for large-scale datasets with millions of images.  present a new framework which consists of two dependent algorithms. First, a new feature is proposed to represent images, which is dubbed compact feature based clustering (CFC). For each image,  first extract cluster centers of local features, and then calculate distribution histograms of local features and statistics of spatial information in each cluster to form compact features based clustering, replacing thousands of local features. It can reduce feature vectors of image representation and enhance the discriminative por of each feature. In addition, an efficient retrieval method is proposed, based on vocabulary tree through compact features based clustering. Extensive experiments on the Ukbench, Holidays, and ImageNet databases demonstrate that our method reduces the memory and computation overhead and improves the retrieval efficiency, while keeping approximate state-of-the-art accuracy."}]