Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Store classification is a challenging task due to the large variation of view, scale, illumination and occlusion. To efficiently distinguish different stores,  introduce two features: Text-Exemplar-Similarity and Hypotheses-ighted-CNN. For the first feature, the similarity with the discriminative characters is used to represent the text information. For the second feature,  first generate a set of object hypotheses. Then,  introduce two priors: edge boundary and repeatness prior to give a higher ight to the hypotheses enclosing the object. After the generation of two features, a simple and efficient optimization method is used to find the best ight for each feature. Extensive experiments are evaluated to verify the superiority of the proposed method.  built a new 9-class store dataset composed of photos and images from the internet. The experiments show that our method is nearly 10% higher than the state-of-art methods.
['Chao Huang', 'Hongliang Li', 'Wei Li', 'Qingbo Wu', 'Linfeng Xu']
I am writing a research paper on a idea Store classification using Text-Exemplar-Similarity and Hypotheses-Weighted-CNN ☆
[{"from": "human", "value": "I am writing a research paper on a idea Store classification using Text-Exemplar-Similarity and Hypotheses-Weighted-CNN \u2606"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Store classification is a challenging task due to the large variation of view, scale, illumination and occlusion. To efficiently distinguish different stores,  introduce two features: Text-Exemplar-Similarity and Hypotheses-ighted-CNN. For the first feature, the similarity with the discriminative characters is used to represent the text information. For the second feature,  first generate a set of object hypotheses. Then,  introduce two priors: edge boundary and repeatness prior to give a higher ight to the hypotheses enclosing the object. After the generation of two features, a simple and efficient optimization method is used to find the best ight for each feature. Extensive experiments are evaluated to verify the superiority of the proposed method.  built a new 9-class store dataset composed of photos and images from the internet. The experiments show that our method is nearly 10% higher than the state-of-art methods."}]