LEADER 05084nam 22007935 450 001 9910805579603321 005 20251027095400.0 010 $a981-9978-82-3 024 7 $a10.1007/978-981-99-7882-3 035 $a(MiAaPQ)EBC31086060 035 $a(Au-PeEL)EBL31086060 035 $a(DE-He213)978-981-99-7882-3 035 $a(MiAaPQ)EBC31093863 035 $a(Au-PeEL)EBL31093863 035 $a(CKB)30113006900041 035 $a(EXLCZ)9930113006900041 100 $a20240124d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aAn Introduction to Image Classification $eFrom Designed Models to End-to-End Learning /$fby Klaus D. Toennies 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (297 pages) 311 08$aPrint version: Toennies, Klaus D. An Introduction to Image Classification Singapore : Springer,c2024 9789819978816 327 $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. 330 $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. 606 $aComputer vision 606 $aMachine learning 606 $aPattern recognition systems 606 $aBiometric identification 606 $aArtificial intelligence$xData processing 606 $aComputer Vision 606 $aMachine Learning 606 $aAutomated Pattern Recognition 606 $aBiometrics 606 $aData Science 606 $aVisiķ per ordinador$2thub 606 $aAprenentatge automātic$2thub 606 $aReconeixement de formes (Informātica)$2thub 606 $aIdentificaciķ biomčtrica$2thub 606 $aProcessament de dades$2thub 608 $aLlibres electrōnics$2thub 615 0$aComputer vision. 615 0$aMachine learning. 615 0$aPattern recognition systems. 615 0$aBiometric identification. 615 0$aArtificial intelligence$xData processing. 615 14$aComputer Vision. 615 24$aMachine Learning. 615 24$aAutomated Pattern Recognition. 615 24$aBiometrics. 615 24$aData Science. 615 7$aVisiķ per ordinador 615 7$aAprenentatge automātic 615 7$aReconeixement de formes (Informātica) 615 7$aIdentificaciķ biomčtrica 615 7$aProcessament de dades 676 $a006.37 700 $aToennies$b Klaus D$01060341 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910805579603321 996 $aAn Introduction to Image Classification$93882665 997 $aUNINA