Incremental updating feature extracion for camera identification

Sensor Pattern Noise (SPN) is an inherent fingerprint of imaging devices, which has been widely used in the tasks of digital camera identification, image classification and forgery detection. In our previous work, a feature extraction method based on PCA denoising concept was applied to extract a set of principal components from the original noise residual. However, this algorithm is inefficient when query cameras are continuously received. To solve this problem, we propose an extension based on Candid Covariance-free Incremental PCA (CCIPCA) and two modifications to incrementally update the feature extractor according to the received cameras. Experimental results show that the PCA and CCIPCA based features both outperform their original features on the ROC performance, and CCIPCA is more efficient on camera updating

Study of webcasting on promoting college students’ consumption

With the advancement of Internet technology and the popularization of mobile network devices, webcasting has become a popular entertainment method, and shopping rewards mediated by webcasting have also become a new consumption marketing model. It is of strategic significance to study the consumption behavior of the main groups of consumers under the live broadcast platform. Based on the social media marketing environment, this paper takes webcasting as the research object, and takes game livecasting as the starting point. The impact of webcasting on consumer behavior, combined with relevant theories, try to put forward corresponding reference strategies

Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation

Accepted by COLING 2020, final camera ready versionPreprin

Fast Online Similarity Search for Uncertain Time Series

To achieve fast retrieval of online data, it is needed for the retrieval algorithm to increase throughput while reducing latency. Based on the traditional online processing algorithm for time series data, we propose a spatial index structure that can be updated and searched quickly in a real-time environment. At the same time, we introduce an adaptive segmentation method to divide the space corresponding to nodes. Unlike traditional retrieval algorithms, for uncertain time series, the distance threshold used for screening will dynamically change due to noise during the search process. Extensive experiments are conducted to compare the accuracy of the query results and the timeliness of the algorithm. The results show that the index structure proposed in this paper has better efficiency while maintaining a similar true positive ratio

Deep Latent Variable Models for Text Modelling

Deep latent variable models is a class of models that parameterise components of probabilistic latent variable models with neural networks. This class of models can capture useful high-level representations of information from the input data, and has been widely applied to many domains (e.g., images, speech, and texts), with tasks ranging from image synthesis to dialogue response generation. For instance, implicit linguistic cues such as topic information are helpful for various text modelling tasks, e.g., language modelling, dialogue response generation. Being able to accurately recognising dialogue acts plays a key role to help generate relevant and meaningful responses for dialogue systems. However, existing deep learning models mostly focus on modelling the interactions between utterances during a conversation (i.e., contextual information), where important implicit linguistic cues (e.g., topic information of the utterances) for recognising dialogue acts have not been considered. This motivates our first model, which is a dual-attention hierarchical recurrent neural network model for dialogue act classification. Compared to other works which focus on modelling contextual information, our model considers, for the first time, both topic information and dialogue act using a dual-attention hierarchical deep learning framework. Experimental results show that our model achieves a better or comparable performance than other baselines. When applying deep latent variable models in the text domain, one can generate diverse texts via randomly sampling latent codes from the trained latent space. However, several noticeable issues of deep latent variable models in the text domain remained unsolved, where one of such issues is KL loss vanishing and has serious effects on the quality of generated texts. To tackle this challenge, we propose a simple and robust Variational Autoencoder (VAE) model to alleviate the KL loss vanishing issue. Specifically, a timestep-wise KL regularisation is proposed and imposed into the encoder of VAE at each timestep. This method does not require careful engineering the objective function of VAE or constructing a more complicated model architecture, as existing models do. In addition, our approach can be easily applied to any types of RNN-based VAEs. Our model is evaluated in the language modelling task and successfully alleviates the KL loss vanishing issue. Our model has also been tested on the dialogue response generation task, which not only avoids the KL loss vanishing issue, but also generates relevant, diverse and contentful responses. Finally, we investigate the low-density latent regions (holes) of VAE in the text domain, a phenomenon which exists in the trained latent space of VAE and leads to low-quality outputs when latent variables are sampled from those areas. In order to provide an in-depth analysis of the holes issue, a novel and efficient tree-based decoder-centric algorithm for the low- density latent regions identification is developed. We further explore how the holes impact the performance of generated texts of VAE models. For instance, we analyse whether the holes are really vacant, which captures no useful information and how the holes are distributed in the latent space

A Shapelet Transform Classification over Uncertain Time Series

A shapelet is a time-series subsequence that can represent local, phase-independent similarity in shape. Time series classification with subsequences can save computing cost, improve computing speed and improve algorithm accuracy. The shapelet-based approaches for time series classification have an advantage of interpretability. Concentrating on uncertain time series, this paper tries to apply the shapelet-based method to classify uncertain time series. Due to the high dimensions of time series, the number of the generated candidate shapelets is generally huge. As a result, the calculation amount is large too. To deal with this problem, in this paper, we introduce a piecewise linear representation (PLR) method for uncertain time series based on key points so that the traditional shapelet discovery algorithm can be improved efficiently. We verify our approach with experiments. The experimental results show that the proposed shapelet algorithm can be used for uncertain time series and it can provide classification accuracy well while reducing time cost

Semi-supervised Learning for Medical Image Segmentation

Medical image segmentation is a fundamental step in many computer aided clinical applications, such as tumour detection and quantification, organ measurement and feature learning, etc. However, manually delineating the target of interest on medical images (2D and 3D) is highly labour intensive and time-consuming, even for clinical experts. To address this problem, this thesis focuses on exploring and developing solutions of interactive and fully automated methods to achieve efficient and accurate medical image segmentation. First of all, an interactive semi-automatic segmentation software is developed for the purpose of efficiently annotating any given medical image in 2D and 3D. By converting the segmentation task into a graph optimisation problem using Conditional Random Field, the software allows interactive image segmentation using scribbles. It can also suggest the best image slice to annotate for segmentation refinement in 3D images. Moreover, an “one size for all” parameter setting is experimentally determined using different image modalities, dimensionalities and resolutions, hence no parameter adjustment is required for different unseen medical images. This software can be used for the segmentation of individual medical images in clinical applications or can be used as an annotation tool to generate training examples for machine learning methods. The software can be downloaded from bit.ly/interactive-seg-tool. The developed interactive image segmentation software is efficient, but annotating a large amount of images (hundreds or thousands) for fully supervised machine learning to achieve automatic segmentation is still time-consuming. Therefore, a semi-supervised image segmentation method is developed to achieve fully automatic segmentation by training on a small number of annotated images. An ensemble learning based method is proposed, which is an encoder-decoder based Deep Convolutional Neural Network (DCNN). It is initially trained using a few annotated training samples. This initially trained model is then duplicated as sub-models and improved iteratively using random subsets of unannotated data with pseudo masks generated from models trained in the previous iteration. The number of sub-models is gradually decreased to one in the final iteration. To the best of our knowledge, this is the first use of ensemble learning and DCNN to achieve semi-supervised learning. By evaluating it on a public skin lesion segmentation dataset, it outperforms both the fully supervised learning method using only annotated data and the state-of-the-art methods using similar pseudo labelling ideas. In the context of medical image segmentation, many targets of interest have common geometric shapes across populations (e.g. brain, bone, kidney, liver, etc.). In this case, deformable image registration (alignment) technique can be applied to annotate an unseen image by deforming an annotated template image. Deep learning methods also advanced the field of image registration, but many existing methods can only successfully align images with small deformations. In this thesis, an encoder-decoder DCNN based image registration method is proposed to deal with large deformations. Specifically, a multi-resolution encoder is applied across different image scales for feature extraction. In the decoder, multi-resolution displacement fields are estimated in each scale and then successively combined to produce the final displacement field for transforming the source image to the target image space. The method outperforms many other methods on a local 2D dataset and a public 3D dataset with large deformations. More importantly, the method is further improved by using segmentation masks to guide the image registration to focus on specified local regions, which improves the performance of both segmentation and registration significantly. Finally, to combine the advantages of both image segmentation and image registration. A unified framework that combines a DCNN based segmentation model and the above developed registration model is developed to achieve semi-supervised learning. Initially, the segmentation model is pre-trained using a small number of annotated images, and the registration model is pre-trained using unsupervised learning of all training images. Subsequently, soft pseudo masks of unannotated images are generated by the registration model and segmentation model. The soft Dice loss function is applied to iteratively improve both models using these pseudo labelled images. It is shown that the proposed framework allows both models to mutually improve each other. This approach produces excellent segmentation results only using a small number of annotated images for training, which is better than the segmentation results produced by each model separately. More importantly, once finished training, the framework is able to perform both image segmentation and image registration in high quality

A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification

Acknowledgment This work is supported by the award made by the UK Engineering and Physical Sciences Research Council (Grant number: EP/P011829/1).PreprintPublisher PD

Learning based forensic techniques for source camera identification

In recent years, multimedia forensics has received rapidly growing attention. One challenging problem of multimedia forensics is source camera identification, the goal of which is to identify the source of a multimedia object, such as digital image and video. Sensor pattern noises, produced by imaging sensors, have been proved to be an effective way for source camera identification. Precisely speaking, the conventional SPN-based source camera identification.has two application models: verification and identification. In the past decade, significant progress has been achieved in the tasks of SPN-based source camera verification and identification. However, there are still many cases requiring solutions beyond the capabilities of the current methods. In this thesis, we considered and addressed two commonly seen but less studied problems. The first problem is the source camera verification with reference SPNs corrupted by scene details. The most significant limitation of using SPN for source camera identification.is that SPN can be seriously contaminated by scene details. Most existing methods consider the contaminations from scene details only occur in query images but not in reference images. To address this issue, we propose a measurement based on the combination of local image entropy and brightness so as to evaluate the quality of SPN contained by different image blocks. Based on this measurement, a context adaptive reference SPN estimator is proposed to address the problem that reference images are contaminated by scene details. The second problem that we considered relates to the high computational complexity of using SPN in source camera identification., which is caused by the high dimensionality of SPN. In order to improve identification.efficiency without degrading accuracy, we propose an effective feature extraction algorithm based on the concept of PCA denoising to extract a small set of components from the original noise residual, which tends to carry most of the information of the true SPN signal. To further improve the performance of this framework, two enhancement methods are introduced. The first enhancement method is proposed to take the advantage of the label information of the reference images so as to better separate different classes and further reduce the dimensionality. Secondly, we propose an extension based on Candid Covariance-free Incremental PCA to incrementally update the feature extractor according to the received images so that there is no need to re-conduct training every time when a new image is added to the database. Moreover, an ensemble method based on the random subspace method and majority voting is proposed in the context of source camera identification.to tackle the performance degradation of PCA-based feature extraction method due to the corruption by unwanted interferences in the training set. The proposed algorithms are evaluated on the challenging Dresden image database and experimental results confirmed their effectiveness