Exploiting Example Structure In Multiple Instance Learning

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Exploiting Example Structure In Multiple Instance Learning

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Author : Rama Krishna Sandeep Pokkunuri
language : en
Publisher:
Release Date : 2011

Exploiting Example Structure In Multiple Instance Learning written by Rama Krishna Sandeep Pokkunuri and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Machine learning categories.

Kernels For Structured Data

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Author : Thomas Gartner
language : en
Publisher: World Scientific
Release Date : 2008-08-29

Kernels For Structured Data written by Thomas Gartner and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-29 with Computers categories.

This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers.

Person Re Identification With Limited Supervision

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Author : Rameswar Panda
language : en
Publisher: Springer Nature
Release Date : 2022-06-01

Person Re Identification With Limited Supervision written by Rameswar Panda and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.

Person re-identification is the problem of associating observations of targets in different non-overlapping cameras. Most of the existing learning-based methods have resulted in improved performance on standard re-identification benchmarks, but at the cost of time-consuming and tediously labeled data. Motivated by this, learning person re-identification models with limited to no supervision has drawn a great deal of attention in recent years. In this book, we provide an overview of some of the literature in person re-identification, and then move on to focus on some specific problems in the context of person re-identification with limited supervision in multi-camera environments. We expect this to lead to interesting problems for researchers to consider in the future, beyond the conventional fully supervised setup that has been the framework for a lot of work in person re-identification. Chapter 1 starts with an overview of the problems in person re-identification and the major research directions. We provide an overview of the prior works that align most closely with the limited supervision theme of this book. Chapter 2 demonstrates how global camera network constraints in the form of consistency can be utilized for improving the accuracy of camera pair-wise person re-identification models and also selecting a minimal subset of image pairs for labeling without compromising accuracy. Chapter 3 presents two methods that hold the potential for developing highly scalable systems for video person re-identification with limited supervision. In the one-shot setting where only one tracklet per identity is labeled, the objective is to utilize this small labeled set along with a larger unlabeled set of tracklets to obtain a re-identification model. Another setting is completely unsupervised without requiring any identity labels. The temporal consistency in the videos allows us to infer about matching objects across the cameras with higher confidence, even withlimited to no supervision. Chapter 4 investigates person re-identification in dynamic camera networks. Specifically, we consider a novel problem that has received very little attention in the community but is critically important for many applications where a new camera is added to an existing group observing a set of targets. We propose two possible solutions for on-boarding new camera(s) dynamically to an existing network using transfer learning with limited additional supervision. Finally, Chapter 5 concludes the book by highlighting the major directions for future research.

Advances In Multimedia Modeling

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Author : Shin'ichi Satoh
language : en
Publisher: Springer
Release Date : 2008-01-22

Advances In Multimedia Modeling written by Shin'ichi Satoh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-01-22 with Computers categories.

This book constitutes the refereed proceedings of the 14th International Multimedia Modeling Conference, MMM 2007, held in Kyoto, Japan, in January 2007. The 23 revised full papers and 24 revised poster papers were carefully reviewed and selected from more than 130 submissions. The papers are organized in topical sections that include material on media understanding, creative media, visual content representation, and video codecs, as well as media retrieval, audio and music.

Advances In Multimedia Information Processing Pcm 2013

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Author : Benoit Huet
language : en
Publisher: Springer
Release Date : 2013-12-09

Advances In Multimedia Information Processing Pcm 2013 written by Benoit Huet and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-09 with Computers categories.

This book constitutes the proceedings of the 14th Pacific-Rim Conference on Multimedia, PCM 2013, held in Nanjing, China, in December 2013. The 30 revised full papers and 27 poster papers presented were carefully reviewed and selected from 153 submissions. The papers cover a wide range of topics in the area of multimedia content analysis, multimedia signal processing and communications and multimedia applications and services.

Structural Syntactic And Statistical Pattern Recognition

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Author : Niels da Vitoria Lobo
language : en
Publisher: Springer
Release Date : 2008-12-02

Structural Syntactic And Statistical Pattern Recognition written by Niels da Vitoria Lobo and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-12-02 with Computers categories.

This volume in the Springer Lecture Notes in Computer Science (LNCS) series contains 98 papers presented at the S+SSPR 2008 workshops. S+SSPR 2008 was the sixth time that the SPR and SSPR workshops organized by Technical Committees, TC1 and TC2, of the International Association for Pattern Rec- nition (IAPR) wereheld as joint workshops. S+SSPR 2008was held in Orlando, Florida, the family entertainment capital of the world, on the beautiful campus of the University of Central Florida, one of the up and coming metropolitan universities in the USA. S+SSPR 2008 was held during December 4–6, 2008 only a few days before the 19th International Conference on Pattern Recog- tion(ICPR2008),whichwasheldin Tampa,onlytwo hoursawayfromOrlando, thus giving the opportunity of both conferences to attendees to enjoy the many attractions o?ered by two neighboring cities in the state of Florida. SPR 2008 and SSPR 2008 received a total of 175 paper submissions from many di?erent countries around the world, thus giving the workshop an int- national clout, as was the case for past workshops. This volume contains 98 accepted papers: 56 for oral presentations and 42 for poster presentations. In addition to parallel oral sessions for SPR and SSPR, there was also one joint oral session with papers of interest to both the SPR and SSPR communities. A recent trend that has emerged in the pattern recognition and machine lea- ing research communities is the study of graph-based methods that integrate statistical andstructural approaches.

Computational Methods For Integrating Vision And Language

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Author : Kenichi Kanatani
language : en
Publisher: Springer Nature
Release Date : 2022-05-31

Computational Methods For Integrating Vision And Language written by Kenichi Kanatani and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.

Modeling data from visual and linguistic modalities together creates opportunities for better understanding of both, and supports many useful applications. Examples of dual visual-linguistic data includes images with keywords, video with narrative, and figures in documents. We consider two key task-driven themes: translating from one modality to another (e.g., inferring annotations for images) and understanding the data using all modalities, where one modality can help disambiguate information in another. The multiple modalities can either be essentially semantically redundant (e.g., keywords provided by a person looking at the image), or largely complementary (e.g., meta data such as the camera used). Redundancy and complementarity are two endpoints of a scale, and we observe that good performance on translation requires some redundancy, and that joint inference is most useful where some information is complementary. Computational methods discussed are broadly organized into ones for simple keywords, ones going beyond keywords toward natural language, and ones considering sequential aspects of natural language. Methods for keywords are further organized based on localization of semantics, going from words about the scene taken as whole, to words that apply to specific parts of the scene, to relationships between parts. Methods going beyond keywords are organized by the linguistic roles that are learned, exploited, or generated. These include proper nouns, adjectives, spatial and comparative prepositions, and verbs. More recent developments in dealing with sequential structure include automated captioning of scenes and video, alignment of video and text, and automated answering of questions about scenes depicted in images.

Dealing With Imbalanced And Weakly Labelled Data In Machine Learning Using Fuzzy And Rough Set Methods

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Author : Sarah Vluymans
language : en
Publisher: Springer
Release Date : 2018-11-23

Dealing With Imbalanced And Weakly Labelled Data In Machine Learning Using Fuzzy And Rough Set Methods written by Sarah Vluymans and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-23 with Computers categories.

This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning. The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields.

Computational Methods For Integrating Vision And Language

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Author : Kobus Barnard
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2016-04-21

Computational Methods For Integrating Vision And Language written by Kobus Barnard and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-21 with Computers categories.

Modeling data from visual and linguistic modalities together creates opportunities for better understanding of both, and supports many useful applications. Examples of dual visual-linguistic data includes images with keywords, video with narrative, and figures in documents. We consider two key task-driven themes: translating from one modality to another (e.g., inferring annotations for images) and understanding the data using all modalities, where one modality can help disambiguate information in another. The multiple modalities can either be essentially semantically redundant (e.g., keywords provided by a person looking at the image), or largely complementary (e.g., meta data such as the camera used). Redundancy and complementarity are two endpoints of a scale, and we observe that good performance on translation requires some redundancy, and that joint inference is most useful where some information is complementary. Computational methods discussed are broadly organized into ones for simple keywords, ones going beyond keywords toward natural language, and ones considering sequential aspects of natural language. Methods for keywords are further organized based on localization of semantics, going from words about the scene taken as whole, to words that apply to specific parts of the scene, to relationships between parts. Methods going beyond keywords are organized by the linguistic roles that are learned, exploited, or generated. These include proper nouns, adjectives, spatial and comparative prepositions, and verbs. More recent developments in dealing with sequential structure include automated captioning of scenes and video, alignment of video and text, and automated answering of questions about scenes depicted in images.

Machine Learning Proceedings 1989

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Author : Alberto Maria Segre
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-06-28

Machine Learning Proceedings 1989 written by Alberto Maria Segre and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Computers categories.

Machine Learning Proceedings 1989