کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4969355 | 1449932 | 2017 | 31 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Store classification using Text-Exemplar-Similarity and Hypotheses-Weighted-CNN
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
Store classification is a challenging task due to the large variation of view, scale, illumination and occlusion. To efficiently distinguish different stores, we introduce two features: Text-Exemplar-Similarity and Hypotheses-Weighted-CNN. For the first feature, the similarity with the discriminative characters is used to represent the text information. For the second feature, we first generate a set of object hypotheses. Then, we introduce two priors: edge boundary and repeatness prior to give a higher weight to the hypotheses enclosing the object. After the generation of two features, a simple and efficient optimization method is used to find the best weight for each feature. Extensive experiments are evaluated to verify the superiority of the proposed method. We 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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Visual Communication and Image Representation - Volume 44, April 2017, Pages 21-28
Journal: Journal of Visual Communication and Image Representation - Volume 44, April 2017, Pages 21-28
نویسندگان
Chao Huang, Hongliang Li, Wei Li, Qingbo Wu, Linfeng Xu,