Multi-scale Discriminant Saliency with Wavelet-based Hidden Markov Tree Modelling

The bottom-up saliency, an early stage of humans' visual attention, can be considered as a binary classification problem between centre and surround classes. Discriminant power of features for the classification is measured as mutual information between distributions of image features and corresponding classes . As the estimated discrepancy very much depends on considered scale level, multi-scale structure and discriminant power are integrated by employing discrete wavelet features and Hidden Markov Tree (HMT). With wavelet coefficients and Hidden Markov Tree parameters, quad-tree like label structures are constructed and utilized in maximum a posterior probability (MAP) of hidden class variables at corresponding dyadic sub-squares. Then, a saliency value for each square block at each scale level is computed with discriminant power principle. Finally, across multiple scales is integrated the final saliency map by an information maximization rule. Both standard quantitative tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed multi-scale discriminant saliency (MDIS) method against the well-know information based approach AIM on its released image collection with eye-tracking data. Simulation results are presented and analysed to verify the validity of MDIS as well as point out its limitation for further research direction.Comment: arXiv admin note: substantial text overlap with arXiv:1301.396

Multimodal Emotion and Sentiment Modeling from Unstructured Big Data: Challenges, Architecture, Techniques

The exponential growth of multimodal content in today's competitive business environment leads to a huge volume of unstructured data. Unstructured big data has no particular format or structure and can be in any form, such as text, audio, images, and video. In this paper, we address the challenges of emotion and sentiment modeling due to unstructured big data with different modalities. We first include an up-to-date review on emotion and sentiment modeling including the state-of-the-art techniques. We then propose a new architecture of multimodal emotion and sentiment modeling for big data. The proposed architecture consists of five essential modules: data collection module, multimodal data aggregation module, multimodal data feature extraction module, fusion and decision module, and application module. Novel feature extraction techniques called the divide-and-conquer principal component analysis (Div-ConPCA) and the divide-and-conquer linear discriminant analysis (Div-ConLDA) are proposed for the multimodal data feature extraction module in the architecture. The experiments on a multicore machine architecture are performed to validate the performance of the proposed techniques.</p

Big data and machine learning with hyperspectral information in agriculture

Hyperspectral and multispectral information processing systems and technologies have demonstrated its usefulness for the improvement of agricultural productivity and practices by providing useful information to farmers and crop managers on the factors affecting crop status and growth. These technologies are widely used in a range of agriculture applications such as crop management, crop yield forecasting, crop disease detection, and the monitoring of agriculture land usage, water, and soil conditions. Hyperspectral information sensing can acquire several hundred spectral bands that cover the electromagnetic spectrum of an observational scene in a single acquisition. The resulting hyperspectral data cube contains a large volume of spatial and spectral information. The hyperspectral sequence of images or video further increases the data generation velocity and volume which lead to the Big data challenges particularly in agricultural remote sensing applications. This paper is structured to first give a comprehensive review of representative studies to provide insights into significant research efforts in agriculture using Big data, machine learning and deep learning with the focus on frameworks or architectures, information processing and analytics with hyperspectral and multispectral data. The potential for utilizing Big data, machine learning and deep learning for hyperspectral and multispectral data in agriculture is very promising. The paper then further explores the potential of using ensemble machine learning and scalable parallel discriminant analysis which takes into consideration the spatial and spectral components for Big data in agriculture. To the best of our knowledge, no similar review study on agriculture with Big data, machine learning and deep learning for hyperspectral and multispectral information processing has been reported. Furthermore, the potential of ensemble machine learning and scalable parallel discriminant analysis has not been explored in agriculture information processing. Experiments and data analytics have been performed on hyperspectral data from agriculture for validation. The results have shown the good performance of our approach.</p

Visual Information Processing in Wireless Sensor Networks: Technology, Trends and Applications

Wireless sensor networks (WSNs) have many applications ranging from environmental monitoring, security management, and medical applications to smart homes.Visual Information Processing in Wireless Sensor Networks: Technology, Trends and Applications provides a central source of reference on visual information processing in wireless sensor network environments and its technology, application, and society issues. This book is an important resource for researchers and academics working in the interdisciplinary domains of wireless sensor network technology and multimedia technology and its related areas, which include image processing, pervasive computing, embedded systems, and computer networks

Application Specific Internet of Things (ASIoTs): Taxonomy, Applications, Use Case and Future Directions

As more and more applications are deployed using the Internet of Things (IoT) technologies, the fragmentation of general purpose IoT technologies to target particular sectors with different requirements is becoming necessary. In this paper, we summarize the latest developments of application-specific IoTs (ASIoTs) (a term to conceptualize the development of IoTs targeted toward specific domains, communications mediums, and industry sectors) in eight representative studies (Internet of Battlefield Things (IoBT), Internet of Medical Things (IoMT), Internet of Animal Things (IoAT), Internet of Waste Things (IoWT), Internet of Underwater Things (IoUWT), Internet of Underground Things (IoUGT), Internet of Nano Things (IoNT), and Internet of Mobile Things (IoMobT) such as the Internet of Vehicles). The paper gives contributions to ASIoTs from three perspectives: First, we offer a basic classification taxonomy for ASIoTs and discuss various representative studies and applications which can be found in the literature; Second, we discuss a use case for a biometrics-based ASIoT (termed IoBioT) for illustration and experiments of face-based biometric recognition on IoBioT are also performed; and Third, we give discussions and future directions for ASIoTs. An objective of this paper is to spur researchers and facilitate the development of ASIoTs for the different user-defined domains, communication mediums, and technology constrained platforms.</p

Embedded Intelligence: Platform Technologies, Device Analytics, and Smart City Applications

This article provides a survey about the state of the art in embedded intelligence (EI) research for smart cities. Currently, a comprehensive survey for EI research for smart cities is not available. This article presents a comprehensive review and discusses representative studies of the emerging and current paradigms for EI with the focus on the enabling technologies, applications, and challenges for smart cities from four areas: 1) first, the overview and classifications of the EI research are presented to show the full spectrum in this area, which also serves as a concise summary of the scope of this article; 2) second, the review and identification of interrelated enabling technologies in the form of EI platform technologies, and EI and device analytics technologies are discussed; 3) third, the article discusses various applications of EI utilizing these technologies and techniques for smart cities; and 4) the article also includes the challenges and insights for future research directions. This comprehensive survey article aims to give useful insights for the research area and motivate researchers toward the development of useful EI solutions for practical deployment in smart cities. </p

Big Feature Data Analytics: Split and Combine Linear Discriminant Analysis (SC-LDA) for Integration Towards Decision Making Analytics

This paper introduces a novel big feature data analytics scheme for integration toward data analytics with decision making. In this scheme, a split and combine approach for a linear discriminant analysis (LDA) algorithm termed SC-LDA is proposed. The SC-LDA replaces the full eigenvector decomposition of LDA with much cheaper eigenvector decompositions on smaller sub-matrices, and then recombines the intermediate results to obtain the exact reconstruction as for the original algorithm. The splitting or decomposition can be further applied recursively to obtain a multi-stage SC-LDA algorithm. The smaller sub-matrices can then be computed in parallel to reduce the time complexity for big data applications. The approach is discussed for an LDA algorithm variation (LDA/QR), which is suitable for the analytics of Big Feature data sets. The projected data vectors into the LDA subspace can then be integrated toward the decision-making process involving classification. Experiments are conducted on real-world data sets to confirm that our approach allows the LDA problem to be divided into the size-reduced sub-problems and can be solved in parallel while giving an exact reconstruction as for the original LDA/QR.</p

Unique Neighborhood Set Parameter Independent Density-Based Clustering With Outlier Detection

Machine learning algorithms such as clustering, classification, and regression typically require a set of parameters to be provided by the user before the algorithms can perform well. In this paper, we present parameter independent density-based clustering algorithms by utilizing two novel concepts for neighborhood functions which we term as unique closest neighbor and unique neighborhood set. We discuss two derivatives of the proposed parameter independent density-based clustering (PIDC) algorithms, termed PIDC-WO and PIDC-O. PIDC-WO has been designed for data sets that do not contain explicit outliers whereas PIDC-O provides very good performance even on data sets with the presence of outliers. PIDC-O uses a two-stage processing where the first stage identifies and removes outliers before passing the records to the second stage to perform the density-based clustering. The PIDC algorithms are extensively evaluated and compared with other well-known clustering algorithms on several data sets using three cluster evaluation criteria (F-measure, entropy, and purity) used in the literature, and are shown to perform effectively both for the clustering and outlier detection objectives.</p