Our brains are wired in a way that they can differentiate between objects, both living and non-living by simply looking at them. In fact, the recognition of objects and a situation through visualization is the fastest way to gather, as well as to relate information. This becomes a pretty big deal for computers where a vast amount of data has to be stuffed into it, before the computer can perform an operation on its own. Ironically, with each passing day, it is becoming essential for machines to identify objects through facial recognition, so that humans can take the next big step towards a more scientifically advanced social mechanism. So, what progress have we really made in that respect?
Computer vision will play a crucial role in visual search, self-driving cars, medicine and many other applications. Success will hinge on collecting and labeling large labeled datasets which will be used to train and test new algorithms. One area that has seen great advances over the last five years is image classification i.e. determining automatically what objects are present in an image. Existing image classification datasets have an equal number of images for each class. However, the real world is long tailed: only a small percentage of classes are likely to be observed; most classes are infrequent or rare.
The concepts of neural architecture search and transfer learning are used under the hood to find the best network architecture and the optimal hyperparameter configuration that minimizes the loss function of the model. This article uses Google Cloud AutoML Vision to develop an end-to-end medical image classification model for Pneumonia Detection using Chest X-Ray Images. The dataset is hosted on Kaggle and can be accessed at Chest X-Ray Images (Pneumonia). Go to the cloud console: https://cloud.google.com/ Setup Project APIs, permissions and Cloud Storage bucket to store the image files for modeling and other assets.
From 2012 to 2016, the New York City Police Department supplied IBM with thousands of surveillance images of unaware New Yorkers for the development of software that could help track down people'of interest,' a shocking report claims. IBM's technology was designed to match stills of individuals with specific physical characteristics, including clothing color, age, gender, hair color, and even skin tone, according to The Intercept. Internal documents and sources involved with the program cited by the report reveal IBM released an early iteration of its video analytics software by 2013, before improving its capabilities over the following years. The report adds to growing concerns on the potential for racial profiling with advanced surveillance technology. From 2012 to 2016, the New York City Police Department supplied IBM with thousands of surveillance images of unaware New Yorkers for the development of software that could help track down people'of interest,' a shocking report claims According to the investigation by The Intercept and the Investigative Fund, the NYPD did not end up using IBM's analytics program as part of its larger surveillance system, and discontinued it by 2016.
As humans, we can distinguish between different objects easily - such as dogs wearing hats, or between oranges and bananas in a bag - but for computers this has been typically much more difficult. A team of Google researchers has developed an advanced image classification and detection algorithm called GoogLeNet, which is twice as effective than previous programs. It is so accurate it can locate and distinguish between a range of object sizes within a single image, and it can also determine an object within, or on top of, an object, within the photo. A team of California-based Google researchers developed GoogLeNet, that uses an advanced classification and detection algorithm to identify object. The software recently placed first in the ImageNet large-scale visual recognition challenge (ILSVRC).