Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields

This work presents a first evaluation of using spatio-temporal receptive fields from a recently proposed time-causal spatio-temporal scale-space framework as primitives for video analysis. We propose a new family of video descriptors based on regional statistics of spatio-temporal receptive field responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain and from object recognition to dynamic texture recognition. The time-recursive formulation enables computationally efficient time-causal recognition. The experimental evaluation demonstrates competitive performance compared to state-of-the-art. Especially, it is shown that binary versions of our dynamic texture descriptors achieve improved performance compared to a large range of similar methods using different primitives either handcrafted or learned from data. Further, our qualitative and quantitative investigation into parameter choices and the use of different sets of receptive fields highlights the robustness and flexibility of our approach. Together, these results support the descriptive power of this family of time-causal spatio-temporal receptive fields, validate our approach for dynamic texture recognition and point towards the possibility of designing a range of video analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure

A comparative study for glioma classification using deep convolutional neural networks

Gliomas are a type of central nervous system (CNS) tumor that accounts for the most of malignant brain tumors. The World Health Organization (WHO) divides gliomas into four grades based on the degree of malignancy. Gliomas of grades I-II are considered low-grade gliomas (LGGs), whereas gliomas of grades III-IV are termed high-grade gliomas (HGGs). Accurate classification of HGGs and LGGs prior to malignant transformation plays a crucial role in treatment planning. Magnetic resonance imaging (MRI) is the cornerstone for glioma diagnosis. However, examination of MRI data is a time-consuming process and error prone due to human intervention. In this study we introduced a custom convolutional neural network (CNN) based deep learning model trained from scratch and compared the performance with pretrained AlexNet, GoogLeNet and SqueezeNet through transfer learning for an effective glioma grade prediction. We trained and tested the models based on pathology-proven 104 clinical cases with glioma (50 LGGs, 54 HGGs). A combination of data augmentation techniques was used to expand the training data. Five-fold cross-validation was applied to evaluate the performance of each model. We compared the models in terms of averaged values of sensitivity, specificity, F1 score, accuracy, and area under the receiver operating characteristic curve (AUC). According to the experimental results, our custom-design deep CNN model achieved comparable or even better performance than the pretrained models. Sensitivity, specificity, F1 score, accuracy and AUC values of the custom model were 0.980, 0.963, 0.970, 0.971 and 0.989, respectively. GoogLeNet showed better performance than AlexNet and SqueezeNet in terms of accuracy and AUC with a sensitivity, specificity, F1 score, accuracy, and AUC values of 1551 Mathematical Biosciences and Engineering Volume 18, Issue 2, 1550–1572. 0.980, 0.889, 0.933, 0.933, and 0.987, respectively. AlexNet yielded a sensitivity, specificity, F1 score, accuracy, and AUC values of 0.940, 0.907, 0.922, 0.923 and 0.970, respectively. As for SqueezeNet, the sensitivity, specificity, F1 score, accuracy, and AUC values were 0.920, 0.870, 0.893, 0.894, and 0.975, respectively. The results have shown the effectiveness and robustness of the proposed custom model in classifying gliomas into LGG and HGG. The findings suggest that the deep CNNs and transfer learning approaches can be very useful to solve classification problems in the medical domain

Feature Extraction

Feature extraction is a procedure aimed at selecting and transforming a data set in order to increase the performance of a pattern recognition or machine learning system. Nowadays, since the amount of data available and its dimension is growing exponentially, it is a fundamental procedure to avoid overfitting and the curse of dimensionality, while, in some cases, allowing a interpretative analysis of the data. The topic itself is a thriving discipline of study, and it is difficult to address every single feature extraction algorithm. Therefore, we provide an overview of the topic, introducing widely used techniques, while at the same time presenting some domain-specific feature extraction algorithms. Finally, as a case, study, we will illustrate the vastness of the field by analysing the usage and impact of feature extraction in neuroimaging

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Harnessing Big Data and Machine Learning for Event Detection and Localization

Anomalous events are rare and significantly deviate from expected pattern and other data instances, making them hard to predict. Correctly and timely detecting anomalous severe events can help reduce risks and losses. Many anomalous event detection techniques are studied in the literature. Recently, big data and machine learning based techniques have shown a remarkable success in a wide range of fields. It is important to tailor big data and machine learning based techniques for each application; otherwise it may result in expensive computation, slow prediction, false alarms, and improper prediction granularity.First, we aim to address the above challenges by harnessing big data and machine learning techniques for fast and reliable prediction and localization of severe events. Firstly, to improve storage failure prediction, we develop a new lightweight and high performing tensor decomposition-based method, named SEFEE, for storage error forecasting in large-scale enterprise storage systems. SEFEE employs tensor decomposition technique to capture latent spatio-temporal information embedded in storage event logs. By utilizing the latent spatio-temporal information, we can make accurate storage error forecasting without training requirements of typical machine learning techniques. The training-free method allows for live prediction of storage errors and their locations in the storage system based on previous observations that had been used in tensor decomposition pipeline to extract meaningful latent correlations. Moreover, we propose an extension to include severity of the errors as contextual information to improve the accuracy of tensor decomposition which in turn improves the prediction accuracy. We further provide detailed characterization of NetApp dataset to provide additional insight into the dynamics of typical large-scale enterprise storage systems for the community.Next, we focus on another application -- AI-driven Wildfire prediction. Wildfires cause billions of dollars in property damages and loss of lives, with harmful health threats. We aim to correctly detect and localize wildfire events in the early stage and also classify wildfire smoke based on perceived pixel density of camera images. Due to the lack of publicly available dataset for early wildfire smoke detection, we first collect and process images from the AlertWildfire camera network. The images are annotated with bounding boxes and densities for deep learning methods to use. We then adapt a transformer-based end-to-end object detection model for wildfire detection using our dataset. The dataset and detection model together form as a benchmark named the Nevada smoke detection benchmark, or Nemo for short. Nemo is the first open-source benchmark for wildfire smoke detection with the focus of the early incipient stage. We further provide a weakly supervised Nemo version to enable wider support as a benchmark