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Event recognition on images by Fine-Tuning of Deep Neural Networks 2nd

Event Recognition on Images by Fine-Tuning of Deep Neural Ne...

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2nd international Scientific conference on Intelligent Information Technologies for Industry (IITI)

作者： Yudin, Dmitry Zeno, Bassel Belgorod State Technol Univ Kostukova Str 46 Belgorod 308012 Russia ITMO Univ Kronverksky Pr 49 St Petersburg 197101 Russia

ISBN: (纸本)9783319683218

The paper considers usage of fine-tuning of the deep neural network ensemble for recognition of 60 event types in the set of 60,000 images from WIDER database. The applied ensemble consists of two deep convolutional neural networks (CNN) using the GoogLeNet architecture, previously trained on other image bases: imageNet and Places. Separately the accuracy of recognition of 10 events was analyzed: "Car Racing", "Ceremony", "Concert", "Demonstration", "Football", "Meeting", "Picnic", "Swimming", "Tennis" and "Traffic". During the ensemble training output layer in the each of deep CNN is replaced to the layer with respectively 10 and 60 neurons and we tune only weights which connect output layer with previous one. The classification accuracy of 10 event classes from the WIDER image database averages 83.22%, for 60 event classes accuracy is 50.4%. In addition, the approach based on the automatic features formation using deep CNN provided a much better recognition quality of social events compared to the choice of features manually (LBP, LDP or HOG) and their further classification by support vector machine. The testing time of the developed ensemble provides the possibility of using the classifier in practical applications of event recognition with a processing speed up to 20 frames per second.

关键词： image recognition Social event Deep learning Neural network Fine-tuning

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A machine learning-based Forensic Discriminator of Pornographic and Bikini images

A Machine Learning-based Forensic Discriminator of Pornograp...

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international Joint conference on Neural Networks (IJCNN)

作者： Moreira, Danilo Coura Fechine, Joseana Macedo Fed Univ Campina Grande UFCG Campina Grande Paraiba Brazil

ISBN: (纸本)9781509060146

The increased use of microcomputers and smart-phones has contributed to social progress, but it has also facilitated the exchange of illegal files, such as child pornography photographs, increasing the demand for digital forensic examination in these devices, which can store more than 300,000 images. Based on the large number of files to be analyzed, it is necessary to use capable algorithms to perform this detection, especially in the most challenging scenarios, such as the distinction between pornographic and bikini images. In this work, we present an approach that improves the "Algorithm for detection of Nudity", proposed by Ap-Apid. Our method improved the performance of this algorithm by using machine learning in extracted features from detected skin regions instead of using static rules to classify this kind of images. In addition, we used detected faces as features in order to mitigate false positives in portrait photos. For conducting the training, validation and testing phases, we used the AIIA-PID4 pornographic data set. Furthermore, we also present a statistical analysis by comparing our approach to two algorithms, one based on the "Algorithm for detection of Nudity" and another proposed by the AIIA-PID4 pornographic data set author. The experimental results showed that we achieved an accuracy of 96.96% and 94.94 in the F1-score metric, increasing the accuracy by 79.19% and 18.21% compared to the referred works, respectively.

关键词： Pornography Detection Nudity Detection Skin Detection pattern recognition image processing Computer Forensics

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To Recognize Families In the Wild: A machine Vision Tutorial 18

To Recognize Families In the Wild: A Machine Vision Tutorial

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26th ACM Multimedia conference (MM)

作者： Robinson, Joseph P. Shao, Ming Fu, Yun Northeastern Univ Boston MA 02115 USA UMass Dartmouth Dartmouth MA 02747 USA

ISBN: (纸本)9781450356657

Automatic kinship recognition has relevance in an abundance of applications. For starters, aiding forensic investigations, as kinship is a powerful cue that could narrow the search space (e.g., knowledge that the "Boston Bombers" were brothers could have helped identify the suspects sooner). In short, there are many beneficiaries that could result from such technologies: whether the consumer (e.g., automatic photo library management), scholar (e.g., historic lineage & genealogical studies), data analyzer (e.g., social-media-based analysis), investigator (e.g., cases of missing children and human trafficking- for instance, it is unlikely that a missing child found online would be in any database, however, more than likely a family member would be), or even refugees. Besides application-based problems, and as already hinted, kinship is a powerful cue that could serve as a face attribute capable of greatly reducing the search space in more general face-recognition problems. In this tutorial, we will introduce the background information, progress leading us up to these points, several current state-of-the-art algorithms spanning various views of the kinship recognition problem (e.g., verification, classification, tri-subject). We will then cover our large-scale Families In the Wild (FIW) image collection, several challenge competitions it as been used in, along with the top performing deep learning approaches. The tutorial will end with a discussion about future research directions and practical use-cases.

关键词： Visual kinship understanding family recognition deep learning familiar feature big data

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Semi-supervised Classification of Concept Drift Data stream Based on Local Component Replacement 1st

Semi-supervised Classification of Concept Drift Data Stream ...

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1st CCF international conference on Artificial Intelligence (CCF-ICAI)

作者： Qin, Keke Wen, Yimin Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Safety Guilin 541004 Peoples R China

ISBN: (纸本)9789811321221;9789811321214

Being compared with traditional data mining, data stream has three distinct characteristics which pose new challenges to machine learning and data mining. These challenges will become more serious when only few instances are labeled in data stream. In the paper, based on the algorithm of SPASC, a strategy of local component replacement for updating classifier pool is proposed. The proposed strategy defines a vector based on local accuracy to evaluate the adaptability of each "component" of a cluster-based classifier to a new chunk and makes the trained cluster-based classifiers in the pool adapt to the current concept better and faster while retaining as much learned knowledge as possible. The proposed algorithm is compared with the state of the art baseline methods on multiple datasets, the experimental results illustrate the effectiveness of the proposed algorithm.

关键词： Semi-supervised learning Local component replacement Ensemble learning

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Colorectal Segmentation using Multiple Encoder-Decoder Network in Colonoscopy images 1

Colorectal Segmentation using Multiple Encoder-Decoder Netwo...

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1st IEEE international conference on Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Ngoc-Quang Nguyen Lee, Sang-Woong Gachon Univ Pattern Recognit & Machine Learning Lab Seongnam South Korea

ISBN: (纸本)9781538695555

Colorectal cancer is the third most common cancer which causes of cancer-related deaths. Therefore, early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper, we propose a polyp segmentation method based on the encoder decoder network. Performance of the method is enhanced by two strategies, we perform a novel database augmentation method for colonoscopy images in the training phase. Besides, in the test phase, we perform an effective prediction by combining multi model to compare the probability of each image that is produced by the network. Evaluation of the proposed method using the ETIS-LariPolypDB [9] database shows that our proposed method outperforms state-of-the-art results.

关键词： colorectal segmentation CRC encoder-decoder multi-model

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An Ensemble Model for Error Modeling with Pseudoinverse learning Algorithm

An Ensemble Model for Error Modeling with Pseudoinverse Lear...

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IEEE international conference on Systems, Man and Cybernetics

作者： Sibo Feng Xiaodan Deng Ping Guo Bo Zhao Qian Yin Hongfeng Wang Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China School of Information Management Dezhou University Shandong China

In Bayesian theory, the maximum posterior estimator uses prior information to estimate the noise in the machine learning model by adding the regularization term. The regularization terms L 1 and L 2 correspond to Laplacian prior and Guassian prior, respectively. In existing deep learning models, in order to use the gradient descent optimization algorithm and achieve good results, most models take L 2 regularization as the regularization term of the network model to fit the complex Guassian noise. However in practice, the Laplace noise and the Guassian noise are both considered as data noise. For multi-layer perceptrons, the difficulty caused by adding L 1 and L 2 into the optimization function of the network is solved by proposing an ensemble model for error modeling through adopting the divide and conquer strategy. First, several base learners are trained to fit different noise distributions of data, then the final results can be obtained by taking the results of each base leaner as new data to train a meta leaner, and get the final results. Among them, coordinate regression method is used to solve L 1 loss, while the pseudo-inverse learning algorithm is employed to solve L 2 loss. Both methods are nongradient optimization algorithms. The comparison results of the model on several data sets show that the proposed ensemble model achieves better performance.

关键词： Training Optimization Data models Neural networks Computational modeling Matrix decomposition Estimation

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A New Real-Time FPGA-Based Implementation of K-Means Clustering for images 1st

A New Real-Time FPGA-Based Implementation of K-Means Cluster...

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1st international conference on Intelligent Manufacturing and Internet of Things (IMIOT) / 5th international conference on Computing for Sustainable Energy and Environment (ICSEE)

作者： Deng, Tiantai Crookes, Danny Siddiqui, Fahad Woods, Roger Queens Univ Belfast Univ Rd Belfast Antrim North Ireland

ISBN: (纸本)9789811323843;9789811323836

As an unsupervised machine-learning algorithm, K-means clustering for images has been widely used in image segmentation. The standard Lloyd's algorithm iteratively allocates all image pixels to clusters until convergence. The processing requirement can be a problem for high-resolution images and/or real-time systems. In this paper, we present a new histogram-based algorithm for K-means clustering, and its FPGA implementation. Once the histogram has been constructed, the algorithm is O(GL) for each iteration, where GL is the number of grey levels. On a Xilinx ZedBoard, our algorithm achieves 140 FPS (640 x 480 images, running at 150 MHz, 4 clusters, 25 iterations), including final image reconstruction. At 100 MHz, it achieves 95 FPS. It is 7.6 times faster than the standard Lloyd's algorithm, but uses only approximately half of the resources, while giving the same results. The more iterations, the bigger the speed-up. For 50 iterations, our algorithm is 10.2 times faster than the Lloyd's approach. Thus for all cases our algorithm achieves real time performance whereas Lloyd's struggles to do so. The number of clusters (up to a user-defined limit) and the initialization method (one of three) can be selected at runtime.

关键词： Unsupervised machine learning Data processing K-means clustering FPGA acceleration

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Patient-Specific Modeling for pattern Identification in Clinical Events Using Data Mining Models and Evolutionary Computing 1st

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1st international conference on Soft Computing: Theories and Applications, SoCTA 2016

作者： Yadav, Sourabh Saroliya, Anil Amity School of Engineering and Technology Amity University JaipurRajasthan India

ISBN: (纸本)9789811056864

Nowadays due to the technological advancement in collecting, processing and producing the information available, it is quite challenging to obtain the relevant information out from various sources of data. According to earlier research in homeopathy, a pattern system of treatment is being followed, which ultimately helps in finding the cure for the disease. One of the main goals of this system of treatment is to individualize the model in a manner that patient-specific prediction can be fixated with the help of pattern identification. By adopting the context of homeopathy system, the aim of this paper focuses on identifying appropriate parameters from a large quantity of raw clinical data, which are responsible for developing celiac disease in the human body. The result is based on the extensive quantitative research of a clinical database, shows that one parameter is correlating with another and such kind of pattern identification will be useful in the curing of celiac disease. © 2018, Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Generation of High Resolution Medical images Using Super Resolution via Sparse Representation 3rd

Generation of High Resolution Medical Images Using Super Res...

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3rd international Afro-European conference for Industrial Advancement (AECIA)

作者： Asif, Muhammad Khan, Shoab Ahmed Hassan, Taimur Akram, M. Usman Shaukat, Arslan Natl Univ Sci & Technol NUST H-12 Islamabad Pakistan

ISBN: (纸本)9783319608341;9783319608334

Generation of high resolution (HR) of images is very important in image processing and computer vision applications such as: CT scan-chest, MRI, X-rays and Optical Coherence Tomography (OCT). In medical imaging field images are very frequently using for analyzing different type of disease symptoms. HR medical images are required for proper diagnosing but due to the hardware limitation it is very difficult. In this paper we propose a HR image generation technique based on image statistic research for single low resolution (LR) input image-using joint dictionary learning. The view of LR image is like down sampled version of HR image and its patches are considered to have a sparse representation with respect to an over complete dictionary. Sparse representation can be recovered under mild condition with compressed sensing perspective. This study explores the use of super resolution (SR), due to learning there is no need to aligned subpixels of different LR images. For validity of our research, we use this technique on OCT and Lungs images and train three dictionaries (i) using OCT images (U) using Lungs images and (iii) using multiple different natural (MDN)-HR images. images are produced with using MDN-HR and for both medical images and also used OCT images dictionary for OCT images and lungs images dictionary for lungs images. We compare our result with previous SR proposed technique-from all aspects dictionary learning approach have superiority. Proposed dictionary learning based technique produced enhanced and upsampled image.

关键词： High resolution Super resolution Sparse representation CT scan of chest Lungs Optical Coherence Tomography Dictionary learning

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1st international conference on Smart Multimedia, ICSM 2018

1st International Conference on Smart Multimedia, ICSM 2018

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1st international conference on Smart Multimedia, ICSM 2018

ISBN: (纸本)9783030043742

The proceedings contain 39 papers. The special focus in this conference is on Smart Multimedia. The topics include: Towards maritime videosurveillance using 4K videos;A heterogeneous image fusion algorithm based on LLC coding;Towards the identification of Parkinson’s disease using only T1 MR images;Atlas-free method of periventricular hemorrhage detection from preterm infants’ T1 MR images;when smart signal processing meets smart imaging;a regularized nonnegative third order tensor decomposition using a primal-dual projected gradient algorithm: Application to 3D fluorescence spectroscopy;Adaptive dithering using curved Markov-Gaussian noise in the quantized domain for mapping SDR to HDR image;a flexible method for time-of-flight camera calibration using random forest;a survey on vision-based hand gesture recognition;affectional ontology and multimedia dataset for sentiment analysis;research on path planning method of an unmanned vehicle in urban road environments;detecting attention in pivotal response treatment video probes;person authentication by air-writing using 3D sensor and time order stroke context;synthetic vision assisted real-time runway detection for infrared aerial images;detection-based online multi-target tracking via adaptive subspace learning;a deep learning approach to predict crowd behavior based on emotion;object tracking in hyperspectral videos with convolutional features and kernelized correlation filter;learning 3DMM deformation coefficients for rendering realistic expression images;semi-supervised adversarial image-to-image translation;a step beyond generative multi-adversarial networks;predicting student seating distribution based on social affinity;adversarial training for dual-stage image denoising enhanced with feature matching;IVUS-Net: An intravascular ultrasound segmentation network;a simplified active calibration algorithm for focal length estimation.

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