Integration Of Content And Context Modalities For Multimedia Big Data Retrieval
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Integration of Content and Context Modalities for Multimedia Big Data Retrieval
With the proliferation of digital photo-capture devices and the development of web technologies, the era of big data has arrived, which poses challenges to process and retrieve vast amounts of data with heterogeneous and diverse dimensionality. In the field of multimedia information retrieval, traditional keyword-based approaches perform well on text data, but it can hardly adapt to image and video due to the fact that a large proportion of this data nowadays is unorganized. This means the textual descriptions of images or videos, also known as metadata, could be unavailable, incomplete or even incorrect. Therefore, Content-Based Multimedia Information Retrieval (CBMIR) has emerged, which retrieves relevant images or videos by analyzing their visual content. Various data mining techniques such as feature selection, classification, clustering and filtering, have been utilized in CBMIR to solve issues involving data imbalance, data quality and size, limited ground truth, user subjectivity, etc. However, as an intrinsic problem of CBMIR, the semantic gap between low-level visual features and high-level semantics is still difficult to conquer. Now, with the rapid popularization of social media repositories, which allows users to upload images and videos, and assign tags to describe them, it has brought new directions as well as new challenges to the area of multimedia information retrieval. As suggested by the name, multimedia is a combination of different content forms that include text, audio, images, videos, etc. A series of research studies have been conducted to take advantage of one modality to compensate the other for various tasks. A framework proposed in this dissertation focuses on integrating visual information and text information, which are referred to as the content and the context modalities respectively, for multimedia big data retrieval. The framework contains two components, namely MCA-based feature selection and sparse linear integration. First, a feature selection method based on Multiple Correspondence Analysis (MCA) is proposed to select features having high correlations with a given class since these features can provide more discriminative information when predicting class labels. This is especially useful for the context modality since the tags assigned to the images or videos by users are known to be very noisy. Selecting discriminative tags can not only remove noise but also reduce feature dimensions. Considering MCA is a technique used to analyze nominal features, a discretization method based on MCA is developed accordingly to handle numeric features. Then the sparse linear integration component takes the selected features from both modalities as the inputs and builds a model that learns a pairwise instance similarity matrix. An optimization problem is formulated to minimize the differences between the similarity matrix generated from the context modality and the differences between the similarity matrix generated from the content modality. Coordinate descent and soft-thresholding can be applied to solve the problem. Compared to the existing approaches, the proposed framework is able to handle noisy and high dimensional features in each of the modalities. Feature correlations are taken into account and no local decision or handcrafted structure is required. The methods presented in this framework can be carried out in parallel, thus parallel and distributed programming framework, such as MapReduce, can be adopted to improve the computing capacity and scale to very large data sets. In the experiment, multiple public benchmark data sets, including collections of images and videos, are used to evaluate each of the components. Comparison with some existing popular approaches verifies the effectiveness of the proposed methods for the task of semantic concept retrieval. Two applications using the proposed methods for content-based recommender systems are presented. The first one uses the sparse linear integration model to find similar items by considering the information from both images and their metadata. Experiment and subjective evaluation are conducted on a self-collected bag data set for online shopping recommendations. The second one employs a topic model to the features extracted from videos and their metadata to determine topics in an unified manner. This application recommends movies with similar distributions in textual topics and visual topics to the users. Benchmark MovieLens1M data set is used for evaluation. Several research directions are identified to improve the framework for various practical challenges.
Quality Software Through Reuse and Integration
This book presents 13 high-quality research articles that provide long sought-after answers to questions concerning various aspects of reuse and integration. Its contents lead to the inescapable conclusion that software, hardware, and design productivity – including quality attributes – is not bounded. It combines the best of theory and practice and contains recipes for increasing the output of our productivity sectors. The idea of improving software quality through reuse is not new. After all, if software works and is needed, why not simply reuse it? What is new and evolving, however, is the idea of relative validation through testing and reuse, and the abstraction of code into frameworks for instantiation and reuse. Literal code can be abstracted. These abstractions can in turn yield similar codes, which serve to verify their patterns. There is a taxonomy of representations from the lowest-level literal codes to their highest-level natural language descriptions. As a result, product quality is improved in proportion to the degree of reuse at all levels of abstraction. Any software that is, in theory, complex enough to allow for self-reference, cannot be certified as being absolutely valid. The best that can be attained is a relative validity, which is based on testing. Axiomatic, denotational, and other program semantics are more difficult to verify than the codes, which they represent! But, are there any limits to testing? And how can we maximize the reliability of software or hardware products through testing? These are essential questions that need to be addressed; and, will be addressed herein.
Advanced Concepts, Methods, and Applications in Semantic Computing
Semantic computing is critical for the development of semantic systems and applications that must utilize semantic analysis, semantic description, semantic interfaces, and semantic integration of data and services to deliver their objectives. Semantic computing has enormous capabilities to enhance the efficiency and throughput of systems that are based on key emerging concepts and technologies such as semantic web, internet of things, blockchain technology, and knowledge graphs. Thus, research that expounds advanced concepts, methods, technologies, and applications of semantic computing for solving challenges in real-world domains is vital. Advanced Concepts, Methods, and Applications in Semantic Computing is a scholarly reference book that provides a sound theoretical foundation for the application of semantic methods, concepts, and technologies for practical problem solving. It is designed as a comprehensive and reliable resource on how semantic-oriented approaches can be used to aid new emergent technologies and tackle real-world problems. Covering topics that include deep learning, machine learning, blockchain technology, and semantic web services, this book is ideal for professionals, academicians, researchers, and students working in the field of semantic computing in various disciplines, including but not limited to software engineering, systems engineering, knowledge engineering, electronic commerce, computer science, and information technology.