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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1911.09301 |
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| _version_ | 1866916005598461952 |
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| author | Doshi, Nishi Shikhenawis, Gitam Mitra, Suman K |
| author_facet | Doshi, Nishi Shikhenawis, Gitam Mitra, Suman K |
| contents | Image Aesthetics Assessment is one of the emerging domains in research. The domain deals with classification of images into categories depending on the basis of how pleasant they are for the users to watch. In this article, the focus is on categorizing the images in high quality and low quality image. Deep convolutional neural networks are used to classify the images. Instead of using just the raw image as input, different crops and saliency maps of the images are also used, as input to the proposed multi channel CNN architecture. The experiments reported on widely used AVA database show improvement in the aesthetic assessment performance over existing approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1911_09301 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Image Aesthetics Assessment using Multi Channel Convolutional Neural Networks Doshi, Nishi Shikhenawis, Gitam Mitra, Suman K Computer Vision and Pattern Recognition Image Aesthetics Assessment is one of the emerging domains in research. The domain deals with classification of images into categories depending on the basis of how pleasant they are for the users to watch. In this article, the focus is on categorizing the images in high quality and low quality image. Deep convolutional neural networks are used to classify the images. Instead of using just the raw image as input, different crops and saliency maps of the images are also used, as input to the proposed multi channel CNN architecture. The experiments reported on widely used AVA database show improvement in the aesthetic assessment performance over existing approaches. |
| title | Image Aesthetics Assessment using Multi Channel Convolutional Neural Networks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/1911.09301 |