Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Product image search aims to retrieve similar product images based on a query image. While deep learning based features work ll in retrieving images of the same category (e.g. “searching for T-shirts from all the clothing images”), they perform poorly when retrieving variants of images within the same category (e.g. “searching for uniform of Chelsea football club from all T-shirts image”), since it requires fine grained matching on image details. ,  present a spatial quantization approach that utilizes spatial pyramid pooling (SPP) and vector of locally aggregated descriptors (VLAD) to extract more discriminative features for style-aware product search. By using the proposed spatial quantization, spatial information is encoded into the image feature to improve the fine grained product image search. Finally, the experiments on a large scale real world dataset provided by Alibaba large-scale image search challenge (ALISC) demonstrate the effectiveness of our method.
['Shuhan Qi', 'Kyaw Zawlin', 'Hanwang Zhang', 'Xuan Wang', 'Ke Gao', 'Lin Yao', 'Tat-seng Chua']
I am writing a research paper on a idea Saliency meets spatial quantization: A practical framework for large scale product search
[{"from": "human", "value": "I am writing a research paper on a idea Saliency meets spatial quantization: A practical framework for large scale product search"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Product image search aims to retrieve similar product images based on a query image. While deep learning based features work ll in retrieving images of the same category (e.g. \u201csearching for T-shirts from all the clothing images\u201d), they perform poorly when retrieving variants of images within the same category (e.g. \u201csearching for uniform of Chelsea football club from all T-shirts image\u201d), since it requires fine grained matching on image details. ,  present a spatial quantization approach that utilizes spatial pyramid pooling (SPP) and vector of locally aggregated descriptors (VLAD) to extract more discriminative features for style-aware product search. By using the proposed spatial quantization, spatial information is encoded into the image feature to improve the fine grained product image search. Finally, the experiments on a large scale real world dataset provided by Alibaba large-scale image search challenge (ALISC) demonstrate the effectiveness of our method."}]