Wen Gao

Jia Li

This book covers fundamental principles and computational approaches relevant to visual saliency computation. As an interdisciplinary problem, visual saliency computation is introduced in this book from an innovative perspective that combines both neurobiology and machine learning. The book is also well-structured to address a wide range of readers, from specialists in the field to general readers interested in computer science and cognitive psychology. With this book, a reader can start from the very basic question of "what is visual saliency?" and progressively explore the problems in detecting salient locations, extracting salient objects, learning prior knowledge, evaluating performance, and using saliency in real-world applications. It is highly expected that this book will spark a great interest of research in the related communities in years to come.

A Machine Learning Perspective

Visual Saliency Computation

Mohd Fauzi bin Sedon

Chaoqun Shen

The 2023 2nd International Conference on Art Design and Digital Technology (ADDT 2023) was successfully held on September 15-17, 2023 in Xi'an, China. ADDT 2023 created a forum for idea sharing and research exchange, opened up new perspectives in related fields and broadened the horizons of all participants. In the conference, 100 individuals around the world took part in the conference. Divided into three parts, the conference agenda covered keynote speeches, oral presentations and online Q&A discussion. Firstly, the keynote speakers were each allocated 30-45 minutes to address their speeches. Then in the oral presentations, the excellent papers we had selected were presented by their authors one by one. We are glad to share with you that we’ve selected a bunch of high-quality papers from the submissions and compiled them into the proceedings after rigorously reviewing them. These papers feature but are not limited to the following topics: Computer Art, Visual Design, Digital Media, Innovative Technology, etc. All the papers have been checked through rigorous review and processes to meet the requirements of publication. We would like to acknowledge all of those who supported ADDT 2023 and made it a great success. In particular, we would like to thank the European Alliance for Innovation (EAI), for the hard work of all its colleagues in publishing this paper volume. We sincerely hope that the ADDT 2023 turned out to be a forum for excellent discussions that enable new ideas to come about, promoting collaborative research.

Proceedings of the 2nd International Conference on Art Design and Digital Technology, ADDT 2023, September 15–17, 2023, Xi’an, China

ADDT 2023

Robert M. Gray

In the current age of information technology, the issues of distributing and utilizing images efficiently and effectively are of substantial concern. Solutions to many of the problems arising from these issues are provided by techniques of image processing, among which segmentation and compression are topics of this book. Image segmentation is a process for dividing an image into its constituent parts. For block-based segmentation using statistical classification, an image is divided into blocks and a feature vector is formed for each block by grouping statistics of its pixel intensities. Conventional block-based segmentation algorithms classify each block separately, assuming independence of feature vectors. Image Segmentation and Compression Using Hidden Markov Models presents a new algorithm that models the statistical dependence among image blocks by two dimensional hidden Markov models (HMMs). Formulas for estimating the model according to the maximum likelihood criterion are derived from the EM algorithm. To segment an image, optimal classes are searched jointly for all the blocks by the maximum a posteriori (MAP) rule. The 2-D HMM is extended to multiresolution so that more context information is exploited in classification and fast progressive segmentation schemes can be formed naturally. The second issue addressed in the book is the design of joint compression and classification systems using the 2-D HMM and vector quantization. A classifier designed with the side goal of good compression often outperforms one aimed solely at classification because overfitting to training data is suppressed by vector quantization. Image Segmentation and Compression Using Hidden Markov Models is an essential reference source for researchers and engineers working in statistical signal processing or image processing, especially those who are interested in hidden Markov models. It is also of value to those working on statistical modeling.

Jia Li

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