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024 7 _a10.1007/978-981-99-7882-3
_2doi
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072 7 _aCOM016000
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100 1 _aToennies, Klaus D.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_996838
245 1 3 _aAn Introduction to Image Classification
_h[electronic resource] :
_bFrom Designed Models to End-to-End Learning /
_cby Klaus D. Toennies.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXVI, 290 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 1. Image Classification - A Computer Vision Task -- Chapter 2. Image Features - Extraction and Categories -- Chapter 3. Feature Reduction -- Chapter 4. Bayesian Image Classification in Feature Space -- Chapter 5. Distance-based Classifiers -- Chapter 6. Decision Boundaries in Feature Space -- Chapter 7. Multi-layer Perceptron for Image Classification -- Chapter 8. Feature Extraction by Convolutional Neural Network -- Chapter 9. Network Set-up for Image Classification -- Chapter 10. Basic Network Training for Image Classification -- Chapter 11. Dealing with Training Deficiencies -- Chapter 12. Learning Effects and Network Decisions.
520 _aImage classification is a critical component in computer vision tasks and has numerous applications. Traditional methods for image classification involve feature extraction and classification in feature space. Current state-of-the-art methods utilize end-to-end learning with deep neural networks, where feature extraction and classification are integrated into the model. Understanding traditional image classification is important because many of its design concepts directly correspond to components of a neural network. This knowledge can help demystify the behavior of these networks, which may seem opaque at first sight. The book starts from introducing methods for model-driven feature extraction and classification, including basic computer vision techniques for extracting high-level semantics from images. A brief overview of probabilistic classification with generative and discriminative classifiers is then provided. Next, neural networks are presented as a means to learn a classification model directly from labeled sample images, with individual components of the network discussed. The relationships between network components and those of a traditional designed model are explored, and different concepts for regularizing model training are explained. Finally, various methods for analyzing what a network has learned are covered in the closing section of the book. The topic of image classification is presented as a thoroughly curated sequence of steps that gradually increase understanding of the working of a fully trainable classifier. Practical exercises in Python/Keras/Tensorflow have been designed to allow for experimental exploration of these concepts. In each chapter, suitable functions from Python modules are briefly introduced to provide students with the necessary tools to conduct these experiments.
650 0 _aComputer vision.
_996839
650 0 _aMachine learning.
_91831
650 0 _aPattern recognition systems.
_93953
650 0 _aBiometric identification.
_911407
650 0 _aArtificial intelligence
_xData processing.
_921787
650 1 4 _aComputer Vision.
_996841
650 2 4 _aMachine Learning.
_91831
650 2 4 _aAutomated Pattern Recognition.
_931568
650 2 4 _aBiometrics.
_932763
650 2 4 _aData Science.
_934092
710 2 _aSpringerLink (Online service)
_996845
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819978816
776 0 8 _iPrinted edition:
_z9789819978830
776 0 8 _iPrinted edition:
_z9789819978847
856 4 0 _uhttps://doi.org/10.1007/978-981-99-7882-3
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
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