Media Content Access: Image-based Filtering
Rehan Ullah, Ali Alkhalifah · International Journal of Advanced Computer Science and Applications · 2018
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, the content filtering is explored from still images perspectives. Thus, this article investigates and analyses 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 (colour) based skin segmentation and detection for detecting the objectionable content in images, therefore, the approach proceeds in the direction of the classical Machine Learning approaches and uses the two well-known classifiers: The Random Forest and the Neural Network. Their fusion is also investigated. Skin colour is analyzed in the YCbCr colour space and in the form of blob analysis. With the “Adult vs Safe” classification, an Accuracy of 0.88 and the low RMSE of 0.313 is achieved, indicating the usefulness of the detection model.