Semi-Automatic Ground Truth Annotation for Benchmarking of Face Detection in Video

Semi-Automatic Ground Truth Annotation for Benchmarking of Face Detection in Video

Dzmitry Tsishkou (Ecole Centrale de Lyon, France), Liming Chen (Ecole Centrale de Lyon, France) and Eugeny Bovbel (Belarusian State University, Belarus)
Copyright: © 2007 |Pages: 21
DOI: 10.4018/978-1-59904-370-8.ch009
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This work presents a method of semi-automatic ground truth annotation for benchmarking of face detection in video. We aim to illustrate the solution to the issue where an image processing and pattern recognition expert is able to label and annotate facial patterns in video sequences at the rate of 7500 frames per hour. We extend these ideas to the semi-automatic face annotation methodology, where all object patterns are categorized into 4 classes in order to increase flexibility of evaluation results analysis. We present a strict guide how to speedup manual annotation process by 30 times and illustrate it with the sample test video sequences that consists of more than 100000 frames, 950 individuals and 75000 facial images. Experimental evaluation of the face detection using the ground truth data, that was semi-automatically labeled, demonstrates effectiveness of current approach both for learning and test stages.

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Table of Contents
Yu-Jin Zhang
Yu-Jin Zhang
Chapter 1
Yu-Jin Zhang
Content-based visual information retrieval (CBVIR), as a new generation (with new concepts, techniques and mechanisms, etc.) of visual information... Sample PDF
Toward High-Level Visual Information Retrieval
Chapter 2
Konstantinos Konstantinidis, Antonios Gasteratos, Ioannis Andreadis
Image Retrieval (IR) is generally known as a collection of techniques for retrieving images on the basis of features, either low-level... Sample PDF
The Imapct of Low-Level Features in Semantic-Based Image
Chapter 3
Enver Sangineto
Among the existing Content Based Image Retrieval (CBIR) techniques for still images based on different perceptual features (e. g., colour, texture... Sample PDF
Shape-Based Image Retrieval By Alignment
Chapter 4
Vyacheslav Parshin, Liming Chen
Automatic video segmentation into semantic units is important to organize an effective content based access to long video. In this work we focus on... Sample PDF
Statistical Audio-Visual Data Fusion for Video Scene Segmentation
Chapter 5
Feng Xu, Yu-Jin Zhang
Image classification and automatic annotation could be treated as effective solutions to enable keyword-based semantic image retrieval.... Sample PDF
A Novel Framework for Image Categorization and Automatic Annotation
Chapter 6
Biren Shah, Ryan Benton, Zonghuan Wu, Vijay Raghavan
When retrieving images, users may find it easier to express the desired semantic content with keywords than visual features. Accurate keyword... Sample PDF
Automatic and Semi-Automatic Techniques for Image Annotation
Chapter 7
Hideyasu Sasaki, Yasushi Kiyoki
The principal concern of this chapter is to provide those in the visual information retrieval community with a methodology which allows them to... Sample PDF
Adaptive Metadata Generation for Integration of Visual and Semantic Information
Chapter 8
Daniel Heesch, Stefan Ruger
Human-computer interaction is increasingly recognised to be an indispensable component of image retrieval systems. A typical form of interaction is... Sample PDF
Interaction Models and Relevance Feedback in Image Retrieval
Chapter 9
Dzmitry Tsishkou, Liming Chen, Eugeny Bovbel
This work presents a method of semi-automatic ground truth annotation for benchmarking of face detection in video. We aim to illustrate the solution... Sample PDF
Semi-Automatic Ground Truth Annotation for Benchmarking of Face Detection in Video
Chapter 10
Stamatia Dasiopoulou, Vasileios Mezaris, Ioannis Kompatsiaris, Michael G. Strintzis
To overcome the limitations of keyword- and content-based visual information access, an ontology-driven framework is developed. Under the proposed... Sample PDF
An Ontology-Based Frameowrk for Semantic Image Analysis and Retrieval
Chapter 11
Hakim Hacid, Abdelkader Djamel Zighed
A multimedia index makes it possible to group data according to similarity criteria. Traditional index structures are based on trees and use the... Sample PDF
A Machine Learning-Based Model for Content-Based Image Retrieval
Chapter 12
Brijesh Verma, Siddhivinayak Kulkarni
This chapter introduces neural networks for Content-Based Image Retrieval (CBIR) systems. It presents a critical literature review of both the... Sample PDF
Neural Networks for Content-Based Image Retrieval
Chapter 13
Hun-Woo Yoo
A new emotion-based video scene retrieval method is proposed in this chapter. Five video features extracted from a video are represented in a... Sample PDF
Semantic-Based Video Scene Retrieval Using Evolutionary Computing
Chapter 14
Antonio Picariello, Maria Luisa Sapino
In this chapter, we focus on those functionalities of multimedia databases that are not present in traditional databases, but are needed when... Sample PDF
Managing Uncertainties in Image Databases
Chapter 15
Mohammed Lamine Kherfi, Djemel Ziou
We present a new approach for improving image retrieval accuracy by integrating semantic concepts. First, images are represented according to... Sample PDF
A Hierarchial Classification Technique for Semantics-Based Image Retrieval
Chapter 16
Joao Magalhaes, Stefan Ruger
Most of the research in multimedia retrieval applications has focused on retrieval by content or retrieval by example. Since the classical review by... Sample PDF
Semantic Multimedia Information Anaylsis for Retrieval Applications
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