Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

IEEE Transactions on Pattern Analysis and Machine Intelligence

In recent years, deep learning has achieved promising success for multimedia quality assessment, especially for image quality assessment (IQA). However, since there exist more complex temporal characteristics in videos, very little work has been done on video quality assessment (VQA) by exploiting powerful deep convolutional neural networks (DCNNs). In this paper, we propose an efficient VQA method named Deep SpatioTemporal video Quality assessor (DeepSTQ) to predict the perceptual quality of various distorted videos in a no-reference manner. In the proposed DeepSTQ, we first extract local and global spatiotemporal features by pre-trained deep learning models without fine-tuning or training from scratch. The composited features consider distorted video frames as well as frame difference maps from both global and local views. Then, the feature aggregation is conducted by the regression model to predict the perceptual video quality. Finally, experimental results demonstrate that our proposed DeepSTQ outperforms state-of-the-art quality assessment algorithms.

Deep Local and Global Spatiotemporal Feature Aggregation for Blind Video Quality Assessment

In this paper we focus our attention on solving the contradiction that it is more and more popular to watch videos through mobile devices and there is an explosive growth of mobile devices with multimedia applications but the display sizes of mobile devices are limited and heterogeneous. We present an intact and generic framework to adapt video presentation (AVP). A novel method for choosing the optimal cropped region is introduced to minimize the information loss over adapting video presentation. In order to ameliorate the output stream, we make use of a group of filters for tracking, smoothing and virtual camera controlling. Experiments indicate that our approach is able to achieve satisfactory results and has obvious superiority especially when the display size is pretty small.

/pdf/adaptive-video-presentation-for-small-display-while-maximize-19tc7lyq77.pdf

Adaptive video presentation for small display while maximize visual information

Blur is one of the most important features related to video quality. Accurate estimating the blur level of a video is a great help to accurately evaluate the video quality. An improved method is provided for estimating the blur level of videos that are compressed by a block based codec, such as H.264/AVC, MPEG2, etc. According to the invention, local blur detection is based on macroblock (MB) edges, a content dependent weighting scheme is employed to decrease the influence from texture and when detecting local blur, the spreading of detection will stop at local minimum and maximum luminance positions.

Method and apparatus for determining blur in an image

Existing learning-based methods for blind image quality assessment (BIQA) are heavily dependent on large amounts of annotated training data, and usually suffer from a severe performance degradation when encountering the domain/distribution shift problem. Thanks to the development of unsupervised domain adaptation (UDA), some works attempt to transfer the knowledge from a label-sufficient source domain to a label-free target domain under domain shift with UDA. However, it requires the coexistence of source and target data, which might be impractical for source data due to the privacy or storage issues. In this paper, we take the first step towards the source-free unsupervised domain adaptation (SFUDA) in a simple yet efficient manner for BIQA to tackle the domain shift without access to the source data. Specifically, we cast the quality assessment task as a rating distribution prediction problem. Based on the intrinsic properties of BIQA, we present a group of well-designed self-supervised objectives to guide the adaptation of the BN affine parameters towards the target domain. Among them, minimizing the prediction entropy and maximizing the batch prediction diversity aim to encourage more confident results while avoiding the trivial solution. Besides, based on the observation that the IQA rating distribution of single image follows the Gaussian distribution, we apply Gaussian regularization to the predicted rating distribution to make it more consistent with the nature of human scoring. Extensive experimental results under cross-domain scenarios demonstrated the effectiveness of our proposed method to mitigate the domain shift.

Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessment

The inherent noises can significantly degrade the accuracy of the attention area detection. In this paper, we present a novel structure of hybrid filter for suppressing noises in saliency maps which is viewed as a preliminary step towards the solution of automatic video region-of-interest determination. The filter presented in our paper makes use of the property of saliency maps and can remove almost all the Gauss noise and pepper-salt noise while preserve the details of attention area. Experimental results demonstrate the efficiency and effectiveness of our approach in extracting the region of interest.

Zhibo Chen

Papers

Deep Local and Global Spatiotemporal Feature Aggregation for Blind Video Quality Assessment

Adaptive video presentation for small display while maximize visual information

Method and apparatus for determining blur in an image

Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessment

Denoising saliency map for region of interest extraction