Book Image

Python Image Processing Cookbook

By : Sandipan Dey
Book Image

Python Image Processing Cookbook

By: Sandipan Dey

Overview of this book

With the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images. This book provides comprehensive coverage of the relevant tools and algorithms, and guides you through analysis and visualization for image processing. With the help of over 60 cutting-edge recipes, you'll address common challenges in image processing and learn how to perform complex tasks such as object detection, image segmentation, and image reconstruction using large hybrid datasets. Dedicated sections will also take you through implementing various image enhancement and image restoration techniques, such as cartooning, gradient blending, and sparse dictionary learning. As you advance, you'll get to grips with face morphing and image segmentation techniques. With an emphasis on practical solutions, this book will help you apply deep learning techniques such as transfer learning and fine-tuning to solve real-world problems. By the end of this book, you'll be proficient in utilizing the capabilities of the Python ecosystem to implement various image processing techniques effectively.
Table of Contents (11 chapters)

Implementing an image search engine

In this recipe, you will learn how to implement a simple image search engine (SE). It will be a search by example system, relying only on the image contents, known as content-based image retrieval (CBIR) systems. The images along with the features extracted are stored so that the system can return similar images (based on the features) during a search. The following describes the four steps of any CBIR system:

  1. Defining an image descriptor (descriptive features of an image)
  2. Indexing search images (for quick retrieval of the images with similar descriptors to the query image. Use an efficient data structure for fast retrieval)
  3. Defining the similarity metric to be used (Euclidean/cosine/chi-squared distance, and so on)
  4. Searching (the user submits a query image to the SE, and the SE extracts features from this query image and uses the indexed features...