# Artificial Intelligence Imagery Analysis Fostering Big Data Analytics
## 2 Artificial Intelligence Imagery Analysis: Background, Evolution, and Illustration
### Azure Cognitive Services from Microsoft
Emotional presence refers to facially communicating emotional states, such as anger, contempt, disgust, fear, happiness, sadness, or surprise. The advancement of innovative technologies has increased the potential of thumbnails. They are particularly significant in fostering online video consumption. New touch-based devices such as smartphones and tablets come with simplified interfaces that favor pictorial over text-based information. Artificial Intelligence Imagery Analysis allows us to analyze about 400,000 thumbnails and videos from YouTube (2005-2015) via Microsoft Cognitive Services APIs for detecting imagery concepts, faces, and emotions in faces within pictures and Tesseract for obtaining data on text within the thumbnails [37], and thereby quantifying the visual content of the thumbnails. For illustration purposes, we provide the raw analysis output for three out of the 400,000 thumbnails in JSON format (see Figures 1-3). For each example, we provide

* **The input image uploaded to the API:** Every image is a thumbnail shown on YouTube. By clicking on the thumbnail, the user can access the YouTube video behind. * **The computer vision API output** follows the general format of first stating an assessment with regard to the input image and then, where applicable, a confidence score (indicating to what degree the API was \"confident\" to make this assessment, with 1 as the highest level of confidence). The output starts with a general categorization of the image and then indicates to what extent the image shows content that is adult or racy. Then, it lists tags assigned either to the image as a whole, or certain objects in the image. What follows is a summarized text description for the image as a whole, for instance \"a group of men on a screen\" and some metadata of the image. The output then lists the faces identified in the image, including coordinates and dimensions, age and gender. In the end, the API output identifies dominant colors, accent colors, whether the image is black and white and what type the image is, for instance clipart or a naturalistic photography. * **The emotion API output** is organized around the faces identified in the input image. For every face, the output first states the coordinates and dimensions. It then provides scores that indicate to what degree the face shows anger, contempt, disgust, fear, happiness, sadness, surprise, or is totally neutral. For each emotion, there is a score between 0 and 1, with 1 indicating that the face expresses the emotion as strongly as possible. The API does not only assign a single dominant emotion to a face, but recognizes when different types of emotions are present in different degrees. The categorization for negative emotions (5 categories) is more fine-grained than for positive emotions (1 category), because human emotions are more complex in the negative range than in the positive range. * The Optical Character Recognition (OCR) output originates from the Tesseract open-source software and first states the two-digit language code for the primary language the OCR engine has looked for in the thumbnail. It further identifies whether all text in the image is rotated by a certain angle and whether the text orientation is \"up\" or \"down\". Afterwards, the output is organized by regions of text, and then by bounding boxes that contain a piece of text with characters that are spatially close, such as a word. For every bounding box, the output identifies coordinates and dimensions and the characters recognized within the box. Figure 1: Sample image 1 and related API output.