Visual Urban Perception with Deep Semantic-Aware Network
| Autor: | Yilong Yin, Yongchao Xu, Cheng Shi, Qizheng Yang, Guangle Song, Chaoran Cui, Xiaohui Han |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Creative visualization
Correctness Visual perception Artificial neural network business.industry Computer science media_common.quotation_subject 020206 networking & telecommunications 02 engineering and technology Object (computer science) Machine learning computer.software_genre Ranking (information retrieval) Perception 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Pairwise comparison Artificial intelligence business computer media_common |
| Zdroj: | MultiMedia Modeling ISBN: 9783030057152 MMM (2) |
| Popis: | Visual urban perception has received a lot attention for its importance in many fields. In this paper we transform it into a ranking task by pairwise comparison of images, and use deep neural networks to predict the specific perceptual score of each image. Distinguished from existing researches, we highlight the important role of object semantic information in visual urban perception through the attribute activation maps of images. Base on this concept, we combine the object semantic information with the generic features of images in our method. In addition, we use the visualization techniques to obtain the correlations between objects and visual perception attributes from the well trained neural network, which further proves the correctness of our conjecture. The experimental results on large-scale dataset validate the effectiveness of our method. |
| Databáze: | OpenAIRE |
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