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Mastering Computer Vision with TensorFlow 2.x

You're reading from   Mastering Computer Vision with TensorFlow 2.x Build advanced computer vision applications using machine learning and deep learning techniques

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Product type Paperback
Published in May 2020
Publisher Packt
ISBN-13 9781838827069
Length 430 pages
Edition 1st Edition
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Author (1):
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Krishnendu Kar Krishnendu Kar
Author Profile Icon Krishnendu Kar
Krishnendu Kar
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Introduction to Computer Vision and Neural Networks
2. Computer Vision and TensorFlow Fundamentals FREE CHAPTER 3. Content Recognition Using Local Binary Patterns 4. Facial Detection Using OpenCV and CNN 5. Deep Learning on Images 6. Section 2: Advanced Concepts of Computer Vision with TensorFlow
7. Neural Network Architecture and Models 8. Visual Search Using Transfer Learning 9. Object Detection Using YOLO 10. Semantic Segmentation and Neural Style Transfer 11. Section 3: Advanced Implementation of Computer Vision with TensorFlow
12. Action Recognition Using Multitask Deep Learning 13. Object Detection Using R-CNN, SSD, and R-FCN 14. Section 4: TensorFlow Implementation at the Edge and on the Cloud
15. Deep Learning on Edge Devices with CPU/GPU Optimization 16. Cloud Computing Platform for Computer Vision 17. Other Books You May Enjoy

Visual Search Using Transfer Learning

Visual search is a method of displaying similar images to one uploaded by a user to a retail website. Similar images are found by transforming an image into a feature vector using a CNN. Visual search has a lot of applications in online shopping as it compliments textual search for a better and more refined way of expressing a user's choice of product. Shoppers like visual discovery and find it something unique that is not available in a traditional shopping experience.

In this chapter, we will use the concepts of deep neural networks learned in Chapter 4, Deep Learning on Images, and Chapter 5, Neural Network Architecture and Models. We will use transfer learning to develop a neural network model for our image classes and apply it for visual search. The exercises in this chapter will help you to develop sufficient practical knowledge...

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