https://scholars.lib.ntu.edu.tw/handle/123456789/581324
Title: | Image aesthetic assessment via deep semantic aggregation | Authors: | Lu K.-H Chang K.-Y CHU-SONG CHEN |
Keywords: | Object recognition; Aesthetic qualities; Baseline models; Deep CNN; Image Aesthetics; OWA operators; Semantic aggregation; Semantics | Issue Date: | 2017 | Start page/Pages: | 232-236 | Source: | 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings | Abstract: | Aesthetic quality estimation of an image is a challenging task. In this paper, we introduce a deep CNN approach to tackle this problem. We adopt the sate-of-the-art object-recognition CNN as our baseline model, and adapt it for handling several high-level attributes. The networks capable of dealing with these high-level concepts are then fused by a learned logical connector for predicting the aesthetic rating. Results on the standard benchmark shows the effectiveness of our approach. ? 2016 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019185105&doi=10.1109%2fGlobalSIP.2016.7905838&partnerID=40&md5=961ee97bc08e5437164cfcd5e812f253 https://scholars.lib.ntu.edu.tw/handle/123456789/581324 |
DOI: | 10.1109/GlobalSIP.2016.7905838 |
Appears in Collections: | 資訊工程學系 |
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