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 insight into how machine learning techniques can be leveraged to refine the visual recognition system, especially in the part of visual feature representation.

Welcome to the 2nd International Conference on Image and Video Retrieval, CIVR2003. The goal of CIVR is to illuminate the state of the art in visual information retrieval and to stimulate collaboration between researchers and practitioners. This year we received 110 submissions from 26 countries. Based upon the reviews of at least 3 members of the program committee, 43 papers were accepted for the research track of the conference. First, we would like to thank all of the members of the Program Committee and the additional referees listed below. Their reviews of the submissions played a pivotal role in the quality of the conference. Moreover,we are grateful to Nicu Sebe and Xiang Zhou for helping to organize the review process; Shih-Fu Chang and Alberto del Bimbo for setting up the practitioner track; and Erwin Bakker for editing the proceedings and designing the conference poster. Special thanks go to our keynote and plenary speakers, Nevenka Dimitrova fromPhilipsResearch,RameshJainfromGeorgiaTech,ChrisPorterfromGetty Images,andAlanSmeatonfromDublinCityUniversity.Furthermore,wewishto acknowledge our sponsors, the Beckman Institute at the University of Illinois at Urbana-Champaign,TsingHuaUniversity,theInstitutionofElectricalEngineers (IEE),PhilipsResearch,andtheLeidenInstituteofAdvancedComputerScience at Leiden University. Finally, we would like to express our thanks to severalpeople who performed important work related to the organization of the conference: Jennifer Quirk and Catherine Zech for the localorganizationat the BeckmanInstitute; Richard Harvey for his help with promotional activity and sponsorship for CIVR2003; andtotheorganizingcommitteeofthe?rstCIVRforsettinguptheinternational mission and structure of the conference.

This second edition provides easy access to important concepts, issues and technology trends in the field of multimedia technologies, systems, techniques, and applications. Over 1,100 heavily-illustrated pages — including 80 new entries — present concise overviews of all aspects of software, systems, web tools and hardware that enable video, audio and developing media to be shared and delivered electronically.

Content-based 3-D object retrieval has attracted extensive attention recently and has applications in a variety of fields, such as, computer-aided design, tele-medicine,mobile multimedia, virtual reality, and entertainment. The development of efficient and effective content-based 3-D object retrieval techniques has enabled the use of fast 3-D reconstruction and model design. Recent technical progress, such as the development of camera technologies, has made it possible to capture the views of 3-D objects. As a result, view-based 3-D object retrieval has become an essential but challenging research topic. View-based 3-D Object Retrieval introduces and discusses the fundamental challenges in view-based 3-D object retrieval, proposes a collection of selected state-of-the-art methods for accomplishing this task developed by the authors, and summarizes recent achievements in view-based 3-D object retrieval. Part I presents an Introduction to View-based 3-D Object Retrieval, Part II discusses View Extraction, Selection, and Representation, Part III provides a deep dive into View-Based 3-D Object Comparison, and Part IV looks at future research and developments including Big Data application and geographical location-based applications. - Systematically introduces view-based 3-D object retrieval, including problem definitions and settings, methodologies, and benchmark testing beds - Discusses several key challenges in view-based 3-D object retrieval, and introduces the state-of-the-art solutions - Presents the progression from general image retrieval techniques to view-based 3-D object retrieval - Introduces future research efforts in the areas of Big Data, feature extraction, and geographical location-based applications

The refereed proceedings of the First Iberial Conference on Pattern Recognition and Image Analysis, IbPria 2003, held in Puerto de Andratx, Mallorca, Spain in June 2003. The 130 revised papers presented were carefully reviewed and selected from 185 full papers submitted. All current aspects of ongoing research in computer vision, image processing, pattern recognition, and speech recognition are addressed.

The book concentrates on the impact of digitalization and digital transformation technologies on the Industry 4.0 and smart factories, how the factory of tomorrow can be designed, built, and run virtually as a digital twin likeness of its real-world counterpart, before the physical structure is actually erected. It highlights the main digitalization technologies that have stimulated the Industry 4.0, how these technologies work and integrate with each other, and how they are shaping the industry of the future. It examines how multimedia data and digital images in particular are being leveraged to create fully virtualized worlds in the form of digital twin factories and fully virtualized industrial assets. It uses BMW Group’s latest SORDI dataset (Synthetic Object Recognition Dataset for Industry), i.e., the largest industrial images dataset to-date and its applications at BMW Group and Idealworks, as one of the main explanatory scenarios throughout the book. It discusses the need of synthetic data to train advanced deep learning computer vision models, and how such datasets will help create the “robot gym” of the future: training robots on synthetic images to prepare them to function in the real world.