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
3 - Visual Recognition Circa 2008
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
Allow me to pose a few questions on the nature of visual recognition. Why do we bother studying it? What are the “things” we recognize? How many things is it useful to recognize? What is the nature of different recognition tasks? What have we learned so far? What are the open problems that face us? I am assuming that the reader has some familiarity with the technical aspects of vision and visual recognition. This is not a survey, and the references are meant to exemplify an idea or an approach; they are not meant to give proper credit to the many excellent people who work in the field. Also, some of the interesting technical issues are not visible from the mile-high perspective I take here and are therefore not mentioned.
What?
What is it that we recognize in images? We recognize both the component elements and the overall scene: materials and surface properties (“leather,” “wet”), objects (“frog,” “corkscrew”) and the gist of the ensemble (“kitchen,” “prairie”). We recognize things both as individuals (“Gandhi,” “my bedroom”) and as members of categories (“people,” “mountainscape”). These distinctions are important because the visual statistics of materials are different from those of objects and scenes. Also, the visual variability of individual objects is different from that of categories; different approaches may be needed to model and recognize each. In the following text, for brevity, when referring to the thing to be recognized, I will often call it “object,” although it could be a material or a scene, an individual or a category.
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- Chapter
- Information
- Object CategorizationComputer and Human Vision Perspectives, pp. 55 - 68Publisher: Cambridge University PressPrint publication year: 2009
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