Smart Content Recognition from Images Using a Mixture of Convolutional Neural Networks

With rapid development of the Internet, web contents become huge. Most of the websites are publicly available, and anyone can access the contents from anywhere such as workplace, home and even schools. Nevertheless, not all the web contents are appropriate for all users, especially children. An example of these contents is pornography images which should be restricted to certain age group. Besides, these images are not safe for work (NSFW) in which employees should not be seen accessing such contents during work. Recently, convolutional neural networks have been successfully applied to many computer vision problems. Inspired by these successes, we propose a mixture of convolutional neural networks for adult content recognition. Unlike other works, our method is formulated on a weighted sum of multiple deep neural network models. The weights of each CNN models are expressed as a linear regression problem learned using Ordinary Least Squares (OLS). Experimental results demonstrate that the proposed model outperforms both single CNN model and the average sum of CNN models in adult content recognition.Comment: To be published in LNEE, Code: github.com/mundher/NSF

Fast and Effective Bag-of-Visual-Word Model to Pornographic Images Recognition Using the FREAK Descriptor

Recently, the Bag of Visual Word (BoVW) has gained enormous popularity between researchers to object recognition. Pornographic image recognition with respect to computational complexity, appropriate accuracy, and memory consumption is a major challenge in the applications with time constraints such as the internet pornography filtering. Most of the existing researches based on the Bow, using the very popular SIFT and SURF algorithms to description and match detected keypoints in the image. The main problem of these methods is high computational complexity due to constructing the high dimensional feature vectors. This research proposed a BoVW based model by adopting very fast and simple binary descriptor FREAK to speed-up pornographic recognition process. Meanwhile, the keypoints are detected in the ROI of images which improves the recognition speed due to eliminating many noise keypoints placed in the image background. Finally, in order to find the most representational visual-vocabulary, different vocabularies are generated from size 150 to 500 for BoVW. Compared with the similar works, the experimental results show that the proposed model has gained remarkable improvement in the terms of computational complexity

Improving Bag of Visual Words Representations with Genetic Programming

The bag of visual words is a well established representation in diverse computer vision problems. Taking inspiration from the fields of text mining and retrieval, this representation has proved to be very effective in a large number of domains. In most cases, a standard term-frequency weighting scheme is considered for representing images and videos in computer vision. This is somewhat surprising, as there are many alternative ways of generating bag of words representations within the text processing community. This paper explores the use of alternative weighting schemes for landmark tasks in computer vision: image categorization and gesture recognition. We study the suitability of using well-known supervised and unsupervised weighting schemes for such tasks. More importantly, we devise a genetic program that learns new ways of representing images and videos under the bag of visual words representation. The proposed method learns to combine term-weighting primitives trying to maximize the classification performance. Experimental results are reported in standard image and video data sets showing the effectiveness of the proposed evolutionary algorithm

Evolving weighting schemes for the Bag of Visual Words

The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g., term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method