Applying deep learning to classify pornographic images and videos

Abstract. It is no secret that pornographic material is now a one-clickaway
from everyone, including children and minors. General social media
networks are striving to isolate adult images and videos from normal
ones. Intelligent image analysis methods can help to automatically
detect and isolate questionable images in media. Unfortunately, these
methods require vast experience to design the classifier including one or
more of the popular computer vision feature descriptors. We propose to
build a classifier based on one of the recently flourishing deep learning
techniques. Convolutional neural networks contain many layers for both
automatic features extraction and classification. The benefit is an easier
system to build (no need for hand-crafting features and classifiers). Additionally,
our experiments show that it is even more accurate than the
state of the art methods on the most recent benchmark dataset.
Conclusions: We proposed applying convolutional neural networks to automatically classify
pornographic images and videos. We showed that our proposed fully automated
solution outperformed the accuracy of hand-crafted feature descriptors solutions.
We are continuing our research to find an even better network architecture for
this problem. Nevertheless, all the successful applications so far rely on supervised
training methods. We expect a new wave of deep learning networks would
emerge by combining supervised and unsupervised methods where a network
can learn from its mistakes while in actual deployment. We believe further research
can also be directed toward allowing machines to consider the context
and overall rhetorical meaning of a video clip while relating them to the images
involved.
Applying deep learning to classify pornographic images and videos

Detecting Distraction of drivers using Convolutional Neural Network

Abstract: With the intervention of social media and internet technology, people are getting more and more careless and distracted while driving which is having a severe detrimental effect on the safety of the driver and his fellow passengers. To provide an effective solution, this paper put forwards a Machine Learning model using Convolutional Neural Networks to not only detect the distracted driver but also identify the cause of his distraction by analyzing the images obtained using the camera module installed inside the vehicle. Convolutional neural networks are known to learn spatial features from images, which can be further examined by fully connected neural networks. The experimental results show a 99% average accuracy in distraction recognition and hence strongly support that our Convolutional neural networks model can be used to identify distraction among the drivers.

Conclusion: Deep learning using Convolutional Neural Networks [3] has become a hot area in Machine Learning research, and it has been extensively used in image classification, voice recognition, etc. In this paper, we use Deep Convolutional Networks for detecting distracted drivers and also identifying the cause of their distraction using the VGG16 [18] and VGG19 [18] model. The above results suggest that the methods discussed in this work can be used to develop a system using which distraction while driving can be detected among drivers. The model proposed can automatically identify any of the mentioned 10 classes of distraction and identify not only basic distraction but also their cause of distraction. With an accuracy of more than 99%, the mentioned system was shown to be efficient and workable. The proposed system can be a part of some Driver State Monitoring System which will effectively monitor the state of the driver while he is driving. Driver state monitoring has been becoming increasingly popular these days and many automobile giants have started adopting such systems as a methodology to prevent accidents. These systems, when installed inside vehicles will raise warnings whenever the driver gets distracted, thus trying to prevent any accidents due to distraction from the driver. Also in this work a significant amount of training time has been shown to be reduced. When pre-trained weights from ImageNet [4] were not used, the training time increased by around 50 times for both VGG16 [18] and VGG19 [18]. A graphical representation of time elapsed is depicted in Fig. 15. This drastic reduction in training time was achieved without diminishing the accuracy of our classification models. In future work as an extension to this work, more categories of distraction can be brought in. Even considering certain specific scenarios, which were not targeted in the present work, such as detecting drowsiness among drivers may also provide an opportunity to widen the scale of the work and build a more efficient system.

Detecting Distraction of drivers using Convolutional Neural Network

Optimization for Deep Learning Algorithms: A Review

ABSTRACT: In past few years, deep learning has received attention in the field of artificial intelligence. This paper reviews three focus areas of learning methods in deep learning namely supervised, unsupervised and reinforcement learning. These learning methods are used in implementing deep and convolutional neural networks. They offered unified computational approach, flexibility and scalability capabilities. The computational model implemented by deep learning is used in understanding data representation with multiple levels of abstractions. Furthermore, deep learning enhanced the state-of-the-art methods in terms of domains like genomics. This can be applied in pathway analysis for modelling biological network. Thus, the extraction of biochemical production can be improved by using deep learning. On the other hand, this review covers the implementation of optimization in terms of meta-heuristics methods. This optimization is used in machine learning as a part of modelling methods.
CONCLUSION
In this review, discussed about deep learning techniques which implementing multiple level of abstraction in feature representation. Deep learning can be characterized as rebranding of artificial neural network. This learning methods gains a large interest among the researchers because of better representation and easier to learn tasks. Even though deep learning is implemented, however there are some issues has been arise. There are easily getting stuck at local optima and computationally expensive. DeepBind algorithm shows that deep learning can cooperate in genomics study. It is to ensure on achieving high level of prediction protein binding affinity. On the other hand, the optimization method which has been discusses consists of several meta-heuristics
methods which can be categorized under evolutionary algorithms. The application of the techniques involvedCRO shows the diversity of optimization algorithm to improve the analysis of modelling techniques. Furthermore, these methods are able to solve the problems arise in conventional neural network as it provides high quality in finding solution in a given search space. The application of optimization methods enable the
extraction of biochemical production of metabolic pathway. Deep learning will gives a good advantage in the biochemical production as it allows high level abstraction in cellular biological network. Thus, the use of CRO will improve the problems arise in deep learning which are getting stuck at local optima and it is computationally expensive. As CRO use global search in the search space to identify global minimum point. Thus, it will improve the training process in the network on refining the weight in order to have minimum error.
Optimization for Deep Learning Algorithms: A Review

Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery

Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery

Abstract: The standard architecture of synthetic aperture radar (SAR) automatic target recognition (ATR) consists of three stages: detection, discrimination, and classification. In recent years, convolutional neural networks (CNNs) for SAR ATR have been proposed, but most of them classify target classes from a target chip extracted from SAR imagery, as a classification for the third stage of SAR ATR. In this report, we propose a novel CNN for end-to-end ATR from SAR imagery. The CNN named verification support network (VersNet) performs all three stages of SAR ATR end-to-end. VersNet inputs a SAR image of arbitrary sizes with multiple classes and multiple targets, and outputs a SAR ATR image representing the position, class, and pose of each detected target. This report describes the evaluation results of VersNet which trained to output scores of all 12 classes: 10 target classes, a target front class, and a background class, for each pixel using the moving and stationary target acquisition and recognition (MSTAR) public dataset.

Conclusion: By applying CNN to the third stage classification in the standard architecture of SAR ATR, the performance has been improved. In order to improve the overall performance of SAR ATR, it is important not only to improve the performance of the third stage classification but also to improve the performance of the first stage detection and the second stage discrimination. In this report, we proposed a CNN based on a new architecture of SAR ATR that consists of a single stage, i.e. endto-end, not the standard architecture of SAR ATR. Unlike conventional CNNs for target classification, the CNN named VersNet inputs a SAR image of arbitrary sizes with multiple classes and multiple targets, and outputs a SAR ATR image representing the position, class, and pose of each detected target. We trained the VersNet to output scores include ten target classes on MSTAR dataset and evaluated its performance. The average IoU for all the pixels of testing (2420 target chips) is over 0.9. Also, the classification accuracy is about 99.5%, if we select the majority class of maximum probability for each pixel as the predicted class.

 

Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery

Very Deep Convolutional Networks for Large-Scale Image Recognition

ABSTRACT In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small ( 3 × 3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision

CONCLUSION In this work we evaluated very deep convolutional networks (up to 19 weight layers) for largescale image classification. It was demonstrated that the representation depth is beneficial for the classification accuracy, and that state-of-the-art performance on the ImageNet challenge dataset can be achieved using a conventional ConvNet architecture (LeCun et al., 1989; Krizhevsky et al., 2012) with substantially increased depth. In the appendix, we also show that our models generalise well to a wide range of tasks and datasets, matching or outperforming more complex recognition pipelines built around less deep image representations. Our results yet again confirm the importance of depth in visual representations.

Very Deep Convolutional Networks for Large-Scale Image Recognition