"Image understanding (IU) is the research area concerned with the design and experimentation of computer systems that integrate explicit models of a visual problem domain with one or more methods for extracting features from images and one or more methods for matching features with models using a control structure. Given a goal, or a reason for looking at a particular scene, these systems produce descriptions of both the images and the world scenes that the images represent."
– Image Understanding, by J.K. Tsotos. In Encyclopedia of Artificial Intelligence. Stuart C. Shapiro, editor. 1987. New York: John Wiley & Sons.
There are a number of popular evaluation metrics for classification other than accuracy such as recall, precision, AUC, F-scores etc. Instead of listing them all here, I think it is best to point you towards some interesting resources that can kick-start your search for answers. Although you might not be using scikit, the metrics remain relevant. It also quite lists differences between binary classification and multi-class classification setting.
A conceptually simple way to recognize images is to directly compare test-set data and training-set data. The accuracy of this approach is limited by the method of comparison used, and by the extent to which the training-set data covers the required configuration space. Here we show that this coverage can be substantially increased using simple strategies of coarse graining (replacing groups of images by their centroids) and sampling (using distinct sets of centroids in combination). We use the MNIST data set to show that coarse graining can be used to convert a subset of training images into about an order of magnitude fewer image centroids, with no loss of accuracy of classification of test-set images by direct (nearest-neighbor) classification. Distinct batches of centroids can be used in combination as a means of sampling configuration space, and can classify test-set data more accurately than can the unaltered training set. The approach works most naturally with multiple processors in parallel.
You don't need always need to build fancy algorithms to tamper with image recognition systems, adding objects in random places will do the trick. In most cases, adversarial models are used to change a few pixels here and there to distort images so objects are incorrectly recognized. A few examples have included stickers that turn images of bananas into toasters, or wearing silly glasses to be fool facial recognition systems into believing you're someone else. Let's not forget the classic case of when a turtle was mistaken as a rifle to really drill home how easy it is to outwit AI. Now researchers from the York University and the University of Toronto, Canada, however, have shown that it's possible to mislead neural networks by copying and pasting pictures of objects into images, too.
Machine Learning has a reputation for demanding lots of data and powerful GPU computations. This leads many people to believe that building custom machine learning models for their specific dataset is impractical without a large investment of time and resources. In fact, you can leverage Transfer Learning on the web to train an accurate image classifier in less than a minute with just a few labeled images. Teaching a machine to classify images has a wide range of practical applications. You may have seen image classification at work in your photos app, automatically suggesting friends or locations for tagging.
In machine learning, the dataset entirely decides the fate of the algorithms. SVM being a supervised learning algorithm requires clean, annotated data. So do we have to depend on others to provide datasets? Creating dataset using Bing/ Google Image search APIS and then labelling them using Dataturks tool simplifies the entire process, and adds flexibility to the process of machine learning. Here is various image classification datasets.
More than two billion images are shared daily in social networks alone. Research shows that it would take a person ten years to look at all the photos shared on Snapchat in the last hour! Media buyers and providers experience difficulty organizing relevant content in groups, parsing components of images/videos, and defining the return on investment from generated content in an efficient way. NVIDIA has many customers and ecosystem partners tackling that problem, using NVIDIA DGX as their preferred platform for deep learning (DL) powered image recognition. One of the notable names among the ecosystem is Imagga, a pioneer in offering a deep learning powered image recognition and image processing solution, built on NVIDIA DGX Station, the world's first personal AI supercomputer.
If you want to dabble with machine learning on the $35 Raspberry Pi you've never had more options. Google offers several kits for carrying out speech and image recognition on the Pi and is readying a USB stick that will turbo charge the Pi's machine-learning capabilities. The tech giant recently boosted the Pi's machine-learning credentials even further by officially supporting its machine learning software framework TensorFlow on the board. If you want to get started with machine learning on the Pi, here's everything you need to know. Google's Artificial Intelligence Yourself (AIY) kits provide a great introduction to machine learning on the Pi.
The increasing speed of unpredictable and chaotic events collapses the tactical decision space for military commanders and first responders. Forward sensor fusion with edge-based processing accelerates recognitional decision making by widening the decision-maker's aperture to a greater number of sensor inputs and a wider array of sensor types. Cognitive computing tools compare signals to patterns to automate decision cues and open the door to meta-cognition, discovery of new patterns, and continuous improvement of decision models. The new BlueforceEDGE Image Recognition Plugin enhance's real-time recognition and speed of decision for operators and fixed and mobile command nodes by recognizing persons of interest as well as object recognition. BlueforceEDGE V3 ("EDGE") software delivers an extensible autonomous agent software platform that enables sensors from multiple manufacturers to be fused into a secure and interoperable stream of data enabling a single pane of glass view of your entire mission space.
Whether you're interested in learning how to apply facial recognition to video streams, building a complete deep learning pipeline for image classification, or simply want to tinker with your Raspberry Pi and add image recognition to a hobby project, you'll need to learn OpenCV somewhere along the way. The truth is that learning OpenCV used to be quite challenging. The documentation was hard to navigate. The tutorials were hard to follow and incomplete. And even some of the books were a bit tedious to work through. The good news is learning OpenCV isn't as hard as it used to be. And in fact, I'll go as far as to say studying OpenCV has become significantly easier. And to prove it to you (and help you learn OpenCV), I've put together this complete guide to learning the fundamentals of the OpenCV library using the Python programming language. Let's go ahead and get started learning the basics of OpenCV and image processing. By the end of today's blog post, you'll understand the fundamentals of OpenCV.