Skip to main content Accessibility help
×
Home
Hostname: page-component-59b7f5684b-vh8gq Total loading time: 0.472 Render date: 2022-09-28T18:35:03.577Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "displayNetworkTab": true, "displayNetworkMapGraph": false, "useSa": true } hasContentIssue true
Object Categorization Object Categorization
Computer and Human Vision Perspectives
Buy print or eBook[Opens in a new window]

Book contents

17 - Towards Integration of Different Paradigms in Modeling, Representation, and Learning of Visual Categories

Published online by Cambridge University Press:  20 May 2010

Sven J. Dickinson
Affiliation:
University of Toronto
Aleš Leonardis
Affiliation:
University of Ljubljana
Bernt Schiele
Affiliation:
Technische Universität, Darmstadt, Germany
Michael J. Tarr
Affiliation:
Carnegie Mellon University, Pennsylvania
Get access

Summary

Introduction

Object representations for categorization tasks should be applicable for a wide range of objects, scalable to handle large numbers of object classes, and at the same time learnable from a few training samples. While such a scalable representation is still illusive today, it has been argued that such a representation should have at least the following properties: it should enable sharing of features (Torralba et al. 2007), it should combine generative models with discriminative models (Fritz et al. 2005; Jaakkola and Haussler 1999), and it should combine both local and global as well as appearanceand shape-based features (Leibe et al. 2005). Additionally, we argue that such object representations should be applicable both for unsupervised learning (e.g., visual object discovery) as well as supervised training (e.g., object detection). Therefore, we extend our previous efforts of hybrid modeling (Fritz et al. 2005) with ideas of unsupervised learning of generative decompositions to obtain an approach that integrates across different paradigms of modeling, representing, and learning of visual categories.

We present a novel method for the discovery and detection of visual object categories based on decompositions using topic models. The approach is capable of learning a compact and low-dimensional representation for multiple visual categories from multiple viewpoints without labeling of training instances. The learnt object components range from local structures over line segments to global silhouette-like descriptions. This representation can be used to discover object categories in a totally unsupervised fashion. Furthermore we employ the representation as the basis for building a supervised multicategory detection system making efficient use of training examples and outperforming pure features-based representations.

Type
Chapter
Information
Object Categorization
Computer and Human Vision Perspectives
, pp. 324 - 347
Publisher: Cambridge University Press
Print publication year: 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×