Semantic Scholar Open Access 2016 506 sitasi

Photo Aesthetics Ranking Network with Attributes and Content Adaptation

Shu Kong Xiaohui Shen Zhe L. Lin R. Měch Charless C. Fowlkes

Abstrak

Real-world applications could benefit from the ability to automatically generate a fine-grained ranking of photo aesthetics. However, previous methods for image aesthetics analysis have primarily focused on the coarse, binary categorization of images into high- or low-aesthetic categories. In this work, we propose to learn a deep convolutional neural network to rank photo aesthetics in which the relative ranking of photo aesthetics are directly modeled in the loss function. Our model incorporates joint learning of meaningful photographic attributes and image content information which can help regularize the complicated photo aesthetics rating problem.

Topik & Kata Kunci

Computer Science Mathematics

Penulis (5)

Shu Kong

Xiaohui Shen

Zhe L. Lin

R. Měch

Charless C. Fowlkes

Format Sitasi

APA MLA BibTeX

Kong, S., Shen, X., Lin, Z.L., Měch, R., Fowlkes, C.C. (2016). Photo Aesthetics Ranking Network with Attributes and Content Adaptation. https://doi.org/10.1007/978-3-319-46448-0_40

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Total Sitasi: 506×
Sumber Database: Semantic Scholar
DOI: 10.1007/978-3-319-46448-0_40
Akses: Open Access ✓