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Title |
Media Content Access: Image based Filtering |
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Author |
Rehan Ullah Khan and Ali Alkhalifah |
| Citation |
Vol. 26 No. 9 pp. 41-46 |
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Abstract |
As the content on the internet contains sensitive adult material, filtering and blocking this content is essential for the social and ethical values of the many societies and organizations. In this paper, we explore this issue from still images perspectives. Thus this article investigates and analyzes the content based filtering which can help in the flagging of the images as adult nature or safe images. As the proposed approach is based on the Chroma (color) based skin segmentation and detection for detecting the objectionable content in images, therefore, we proceed in the direction of the classical Machine Learning approach and use the two well-known classifiers: The Random Forest and the Neural Network. We also investigate their fusion. We analyze skin color in the YCbCr color space and in the form of blob analysis. With the Adult vs Safe classification, we get an Accuracy of 0.88 and the low RMSE of 0.313, indicating the usefulness of our detection model. |
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Keywords |
Skin, color detection, filtering, machine learning |
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URL |
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|
Title |
Media Content Access: Image based Filtering |
|
Author |
Rehan Ullah Khan and Ali Alkhalifah |
| Citation |
Vol. 26 No. 10 pp. 41-46 |
|
Abstract |
As the content on the internet contains sensitive adult material, filtering and blocking this content is essential for the social and ethical values of the many societies and organizations. In this paper, we explore this issue from still images perspectives. Thus this article investigates and analyzes the content based filtering which can help in the flagging of the images as adult nature or safe images. As the proposed approach is based on the Chroma (color) based skin segmentation and detection for detecting the objectionable content in images, therefore, we proceed in the direction of the classical Machine Learning approach and use the two well-known classifiers: The Random Forest and the Neural Network. We also investigate their fusion. We analyze skin color in the YCbCr color space and in the form of blob analysis. With the Adult vs Safe classification, we get an Accuracy of 0.88 and the low RMSE of 0.313, indicating the usefulness of our detection model. |
|
Keywords |
Skin, color detection, filtering, machine learning |
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URL |
