Sebe / Cohen / Garg Machine Learning in Computer Vision

1. Auflage 2005
ISBN: 978-1-4020-3275-2
Verlag: Springer Netherland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 29, 253 Seiten, eBook

Reihe: Computational Imaging and Vision

The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications.
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The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.

Zielgruppe

Research

Autoren/Hrsg.

Sebe, Nicu

Cohen, Ira

Garg, Ashutosh

Huang, Thomas S.

Inhaltsverzeichnis

Theory: Probabilistic Classifiers.- Theory: Generalization Bounds.- Theory: Semi-Supervised Learning.- Algorithm: Maximum Likelihood Minimum Entropy HMM.- Algorithm: Margin Distribution Optimization.- Algorithm: Learning the Structure of Bayesian Network Classifiers.- Application: Office Activity Recognition.- Application: Multimodal Event Detection.- Application: Facial Expression Recognition.- Application: Bayesian Network Classifiers for Face Detection.

Leseproben

Chapter 11

APPLICATION: BAYESIAN NETWORK CLASSIFIERS FOR FACE DETECTION (p.211)

Images containing faces are essential to intelligent vision-based human computer interaction. To buld fully automated systems that analyze the information contained in face images, robust and ef.cient face detection algorithms are required. Among the face detection methods, the ones based on learning algorithms have attracted much attention recently and have demonstrated excellent results.

This chapter presents a discussion on semi-supervised learning of probabilistic mixture model classi.ers for face detection. Based on our complete theoretical analysis of semi-supervised learning using maximum likelihood presented in Chapter 4 we discuss the possibility of structure learning of Bayesian networks for face detection. We show that learning the structure of Bayesian networks classi.ers enables learning of good classi.ers for face detection with a small labeled set and a large unlabeled set.

1. Introduction
Many of the recent applications designed for human-computer intelligent interaction applications have used the human face as an input. Systems that perform face tracking for various applications, facial expression recognition and pose estimation of faces all rely on detection of human faces in the video frames [Pentland, 2000]. The rapidly expanding research in face processing is based on the premise that information about user’s identity, state, and intend can be extracted from images and that computers can react accordingly, e.g., by observing a person’s facial expression.

In the last years, face and facial expression recognition have attracted much attention despite the fact that they have been studied for more than 20 years by psychophysicists, neuroscientists, and engineers. Many research demonstrations and commercial applications have been developed from these efforts. Given an arbitrary image, the goal of face detection is to automatically locate a human face in an image or video, if it is present. Face detection in a general setting is a challenging problem due to the variability in scale, location, orientation (up-right, rotated), and pose (frontal, pro.le). Facial expression, occlusion, and lighting conditions also change the overall apprearance of faces. Yang et al. [Yang et al., 2002] summarize in their comprehensive survey the challenges associated with face detection:

- Pose. The images of a face vary due to the relative camera-face pose (frontal, 45 degree, pro.le, upside down), and some facial features (e.g., an eye or the nose) may become partially or wholly occluded.

- Presence or absence of structural components. Facial features such as beards, mustaches, and glasses may or may not be present and there is a great deal of variability among these components including shape, color, and size.

- Facial expression. The appearance of faces is directly affected by the facial expression of the persons.

-Occlusion. Faces may be partially occluded by other objects. In an image with a group of people, some faces may partially occlude other faces.

- Image orientation. Face images directly vary for different rotations about the camera’s optical axis.

-Imaging conditions. When the image is formed, factors such as lighting (spectra, source distribution and intensity) and camera characteristics (sensor response, lenses) affect the appearance of a face.