Image recognition is an emerging data mining application field involved in processing, analyzing, and categorizing visual objects such as pictures. In the process of recognition (or categorization), images are first transformed into a multidimensional feature space and then, using machine-learning techniques, are categorized into a finite number of classes. Application areas of image recognition and categorization range from agriculture to homeland security, personalized marketing to environmental protection. Image recognition is an integral part of an artificial intelligence field called computer vision. As a technological discipline, computer vision seeks to develop computer systems that are capable of “ seeing” and reacting to their environment. Examples of applications of computer vision include systems for process automation (industrial robots), navigation (autonomous vehicles), monitoring/ detecting (visual surveillance), searching and sorting visuals (indexing databases of images and image sequences), engaging (computer–human interaction), and inspection (manufacturing processes). While the field of visual recognition and category recognition has been progressing rapidly, much remains to be done to reach human-level performance. Current approaches are capable of dealing with only a limited number of categories (100 or so categories) and are computationally expensive. Many machine-learning techniques (including ANN, SVM, and kNN) are used to develop computer systems for visual recognition and categorization. Though commendable results have been obtained, generally speaking, none of these tools in their current form is capable of developing systems that can compete with humans.

Questions for Discussion
1. Why is image recognition/classification a worthy but difficult problem?
2. How can kNN be effectively used for image recognition/ classification applications?