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This book illustrates utilization of fast transform, sparse representation and low rank analysis as tool in multidimensional signal processing and focuses on discrete cosine transform, optimization of double tree wavelet transform in coding and noise reduction, self-return compression perception of image signal. With orignal research results, the book is an essential reference for electrical engineering researchers and engineers.

Learning-Based Local Visual Representation and Indexing, reviews the state-of-the-art in visual content representation and indexing, introduces cutting-edge techniques in learning based visual representation, and discusses emerging topics in visual local representation, and introduces the most recent advances in content-based visual search techniques.

• Discusses state-of-the-art procedures in learning-based local visual representation.
• Shows how to master the basic techniques needed for building a large-scale visual search engine and indexing system
• Provides...

Many underlying relationships among data can be represented using graphs, for example in the areas including computer vision, molecular chemistry, molecular biology, etc. In the last decade, methods like graph-based learning and neural network methods have been developed to process such data, they are particularly suitable for handling relational learning tasks. In many real-world problems, however, relationships among the objects of our interest are more complex than pair-wise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which...

Many underlying relationships among data can be represented using graphs, for example in the areas including computer vision, molecular chemistry, molecular biology, etc. In the last decade, methods like graph-based learning and neural network methods have been developed to process such data, they are particularly suitable for handling relational learning tasks. In many real-world problems, however, relationships among the objects of our interest are more complex than pair-wise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which...