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Informations générales |
| Auteur |
Behrad, Alireza; Salehpour, Mehdi; Saiedi, Mahmoud; Barati, Mahdi Nasrollah |
| Publié |
InTech Open Access Publisher, 2013
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| Abstract |
In this paper, a novel approach for identifying
normal and obscene videos is proposed. In order to
classify different episodes of a video independently and
discard the need to process all frames, first, key frames
are extracted and skin regions are detected for groups of
video frames starting with key frames. In the second step,
three different features including 1‐ structural features
based on single frame information, 2‐ features based on
spatiotemporal volume and 3‐motion‐based features, are
extracted for each episode of video. The PCA‐LDA
method is then applied to reduce the size of structural
features and select more distinctive features. For the final
step, we use fuzzy or a Weighted Support Vector
Machine (WSVM) classifier to identify video episodes.
We also employ a multilayer Kohonen network as an
initial clustering algorithm to increase the ability to
discriminate between the extracted features into two
classes of videos. Features based on motion and
periodicity characteristics increase the efficiency of the
proposed algorithm in videos with bad illumination and
skin colour variation. The proposed method is evaluated
using 1100 videos in different environmental and
illumination conditions. The experimental results show a
correct recognition rate of 94.2% for the proposed
algorithm. |
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International Journal of Advanced Robotic Systems
Auteur: Ottaviano, Erika; Ceccarelli, Marco; Husty, Manfred; Yu, Sung-Hoon; Kim, Yong-Tae; Park, Chang-Woo; Hyun, Chang-Ho; Chen, Xiulong; Feng, Weiming; Sun, Xianyang; Gao, Qing; Grigorescu, Sorin M.; Pozna, Claudiu; Liu, Wanli; Zhankui, Wang; Guo, Meng; Fu, Guoyu; Zhang, Jin; Chen, Wenyuan; Peng, Fengchao; Yang, Pei; Chen, Chunlin; Ding, Rui; Yu, Junzhi; Yang, Qinghai; Tan, Min; Polden, Joseph; Pan, [...]
Publié: 2004
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