Book contents
- Frontmatter
- Contents
- Preface
- Contributors
- 1 The Evolution of Object Categorization and the Challenge of Image Abstraction
- 2 A Strategy for Understanding How the Brain Accomplishes Object Recognition
- 3 Visual Recognition Circa 2008
- 4 On What It Means to See, and WhatWe Can Do About It
- 5 Generic Object Recognition by Inference of 3-D Volumetric Parts
- 6 What Has fMRI Taught Us About Object Recognition?
- 7 Object Recognition Through Reasoning About Functionality: A Survey of Related Work
- 8 The Interface Theory of Perception: Natural Selection Drives True Perception to Swift Extinction
- 9 Words and Pictures: Categories, Modifiers, Depiction, and Iconography
- 10 Structural Representation of Object Shape in the Brain
- 11 Learning Hierarchical Compositional Representations of Object Structure
- 12 Object Categorization in Man, Monkey, and Machine: Some Answers and Some Open Questions
- 13 Learning Compositional Models for Object Categories from Small Sample Sets
- 14 The Neurophysiology and Computational Mechanisms of Object Representation
- 15 From Classification to Full Object Interpretation
- 16 Visual Object Discovery
- 17 Towards Integration of Different Paradigms in Modeling, Representation, and Learning of Visual Categories
- 18 Acquisition and Disruption of Category Specificity in the Ventral Visual Stream: The Case of Late Developing and Vulnerable Face-Related Cortex
- 19 Using Simple Features and Relations
- 20 The Proactive Brain: Using Memory-Based Predictions in Visual Recognition
- 21 Spatial Pyramid Matching
- 22 Visual Learning for Optimal Decisions in the Human Brain
- 23 Shapes and Shock Graphs: From Segmented Shapes to Shapes Embedded in Images
- 24 Neural Encoding of Scene Statistics for Surface and Object Inference
- 25 Medial Models for Vision
- 26 Multimodal Categorization
- 27 Comparing 2-D Images of 3-D Objects
- Index
- Plate section
26 - Multimodal Categorization
Published online by Cambridge University Press: 20 May 2010
- Frontmatter
- Contents
- Preface
- Contributors
- 1 The Evolution of Object Categorization and the Challenge of Image Abstraction
- 2 A Strategy for Understanding How the Brain Accomplishes Object Recognition
- 3 Visual Recognition Circa 2008
- 4 On What It Means to See, and WhatWe Can Do About It
- 5 Generic Object Recognition by Inference of 3-D Volumetric Parts
- 6 What Has fMRI Taught Us About Object Recognition?
- 7 Object Recognition Through Reasoning About Functionality: A Survey of Related Work
- 8 The Interface Theory of Perception: Natural Selection Drives True Perception to Swift Extinction
- 9 Words and Pictures: Categories, Modifiers, Depiction, and Iconography
- 10 Structural Representation of Object Shape in the Brain
- 11 Learning Hierarchical Compositional Representations of Object Structure
- 12 Object Categorization in Man, Monkey, and Machine: Some Answers and Some Open Questions
- 13 Learning Compositional Models for Object Categories from Small Sample Sets
- 14 The Neurophysiology and Computational Mechanisms of Object Representation
- 15 From Classification to Full Object Interpretation
- 16 Visual Object Discovery
- 17 Towards Integration of Different Paradigms in Modeling, Representation, and Learning of Visual Categories
- 18 Acquisition and Disruption of Category Specificity in the Ventral Visual Stream: The Case of Late Developing and Vulnerable Face-Related Cortex
- 19 Using Simple Features and Relations
- 20 The Proactive Brain: Using Memory-Based Predictions in Visual Recognition
- 21 Spatial Pyramid Matching
- 22 Visual Learning for Optimal Decisions in the Human Brain
- 23 Shapes and Shock Graphs: From Segmented Shapes to Shapes Embedded in Images
- 24 Neural Encoding of Scene Statistics for Surface and Object Inference
- 25 Medial Models for Vision
- 26 Multimodal Categorization
- 27 Comparing 2-D Images of 3-D Objects
- Index
- Plate section
Summary
Introduction
Imagine entering a room for the very first time (Fig. 26.1) and then being asked to look around and to see what is in it. The first glance already tells you what kind of room it is (in our case it clearly is a scene set in a museum); immediately afterwards you begin to notice objects in the room (the prominent statue in the foreground, several other statues on pedestals, paintings on the walls, etc.). Your attention might be drawn more towards certain objects first and then wander around taking in the remaining objects. Very rarely will your visual system need to pause and take more time to investigate what a particular object is – even more rarely will it not be able to interpret it at all (perhaps the four-horned statue in the back of the room will be confusing at first, but it still can be interpreted as a type of four-legged, hoofed animal). The remarkable ability of the (human) visual system to quickly and robustly assign labels to objects (and events) is called categorization.
The question of how we learn to categorize objects and events has been at the heart of cognitive and neuroscience research for the last decades. At the same time, advances in the field of computational vision – both in terms of the algorithms involved as well as in the capabilites of today's computers – have made it possible to begin to look at how computers might solve the difficult problem of categorization. In this chapter, we will therefore address some of the key challenges in categorization from a combined cognitive and computational perspective.
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- Chapter
- Information
- Object CategorizationComputer and Human Vision Perspectives, pp. 488 - 501Publisher: Cambridge University PressPrint publication year: 2009