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portada active exploration of training data for improved object detection
Type
Physical Book
Author
Publisher
Year
2012
Language
English
Pages
112
Format
Paperback
Dimensions
22.9x15.2x0.7 cm
Weight
0.18 kg.
ISBN
3848490803
ISBN13
9783848490806
Categories

active exploration of training data for improved object detection

Kenji Okuma (Author) · LAP Lambert Academic Publishing · Paperback

active exploration of training data for improved object detection - Okuma, Kenji

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Synopsis "active exploration of training data for improved object detection"

This book concerns the problem of object detection, which is defined as finding all instances of an object class of interest and fitting each of them with a tight bounding window. This seemingly easy task for humans is still extremely difficult for machines. However, recent advances in object detection have enabled machines to categorize many classes of objects. Statistical models are often used for representing an object class of interest. These models learn from extensive training sets and generalize with low error rates to unseen data in a highly generic manner. But, these statistical methods have a major drawback in that they require a large amount of training data. We approach this problem by making the process of acquiring labels less tedious and less costly by reducing human labelling effort. Throughout this book, we explore means of efficient label acquisition for realizing cheaper training, faster development time, and higher-performance of object detectors.

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