Scene Nudity Level Detection With Deep Nets
| Autor: | Gozde Bozdagi Akar, Ersin Esen, Savas Ozkan, Ilkay Atil |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry Generalization 020207 software engineering Pattern recognition 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Constant false alarm rate Set (abstract data type) Support vector machine 0202 electrical engineering electronic engineering information engineering False alarm Artificial intelligence business computer Dropout (neural networks) 0105 earth and related environmental sciences |
| Zdroj: | SIU |
| Popis: | In this paper, we present an approach that can detect scene nudity level with high precision using different deep net configurations. For this purpose, a recent approach [1] which has intense and very deep convolution layers is used. During net modelling, we strive to obtain most successful net configuration by comparing different Dropout models and image sizes -64 × 64, 128 × 128-. Additionally, leveraging the generalization capability of Support Vector Machine (SVM), improvement on success rate is demonstrated by retraining the features obtained at different output levels of the nets with SVM. At test and training stages, scene is investigated under three nudity levels: regular, semi-nudity and full-nudity. In order to evaluate false alarm rates of the net models, tests are conducted on different datasets which are SUN2012, LEAR Human and a dataset contains only semi-nudity samples besides validation set determined for each class. The results indicate that high precision rates can be achieved with low false alarm rate exploiting deep net models. |
| Databáze: | OpenAIRE |
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