Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The potential value of hashing techniques has led to it becoming one of the most active research areas in computer vision and multimedia. Hover, most existing hashing methods for image search and retrieval are based on global representations, e.g., GIST, which lack the analysis of the intrinsic geometric property of local features and heavily limit the effectiveness of the hash code. ,  propose a novel supervised hashing method called Local Feature Binary Coding (LFBC) for projecting local feature descriptors from a high-dimensional feature space to a lor-dimensional Hamming space via compact bilinear projections rather than a single large projection matrix. LFBC takes the matrix expression of local features as input and preserves the feature-to-feature and image-to-class structures simultaneously. Experimental results on challenging datasets including Caltech-256, SUN397 and NUS-WIDE demonstrate the superiority of LFBC compared with state-of-the-art hashing methods.
['Li Liu', 'Mengyang Yu', 'Ling Shao']
I am writing a research paper on a idea Local Feature Binary Coding for Approximate Nearest Neighbor Search
[{"from": "human", "value": "I am writing a research paper on a idea Local Feature Binary Coding for Approximate Nearest Neighbor Search"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The potential value of hashing techniques has led to it becoming one of the most active research areas in computer vision and multimedia. Hover, most existing hashing methods for image search and retrieval are based on global representations, e.g., GIST, which lack the analysis of the intrinsic geometric property of local features and heavily limit the effectiveness of the hash code. ,  propose a novel supervised hashing method called Local Feature Binary Coding (LFBC) for projecting local feature descriptors from a high-dimensional feature space to a lor-dimensional Hamming space via compact bilinear projections rather than a single large projection matrix. LFBC takes the matrix expression of local features as input and preserves the feature-to-feature and image-to-class structures simultaneously. Experimental results on challenging datasets including Caltech-256, SUN397 and NUS-WIDE demonstrate the superiority of LFBC compared with state-of-the-art hashing methods."}]