Store classification using Text-Exemplar-Similarity and Hypotheses-Weighted-CNN
| Autor: | Qingbo Wu, Linfeng Xu, Wei Li, Hongliang Li, Chao Huang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Similarity (geometry)
Boundary (topology) 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Set (abstract data type) Discriminative model Prior probability 0202 electrical engineering electronic engineering information engineering Media Technology Electrical and Electronic Engineering 0105 earth and related environmental sciences Mathematics Contextual image classification business.industry Pattern recognition Object (computer science) Feature (computer vision) Signal Processing 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence Data mining business computer |
| Zdroj: | Journal of Visual Communication and Image Representation. 44:21-28 |
| ISSN: | 1047-3203 |
| DOI: | 10.1016/j.jvcir.2017.01.011 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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