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Lecture

Social Image Systems: From Past to Present

In course

DH-500: Computational Social Media

The course integrates concepts from media studies, machine learning, multimedia, and network science to characterize social practices and analyze content in platforms like Twitter/X, Instagram, YouTube, and TikTok. Students will learn computational methods ...

Description

This lecture provides an overview of social image systems, starting with a look at image sharing practices in the 20th century. It delves into research on platforms like Flickr, Instagram, and Snapchat, exploring the role of computer vision in enabling these systems. The lecture also covers visual classification using deep learning models like ResNet, GoogleNet, and VGG, with a focus on the ImageNet dataset. Additionally, it discusses the rise of selfies on platforms like Instagram, the use of biometrics as a service, and the impact of faces on engagement metrics. The lecture concludes with insights from large-scale selfie analysis and the Selfiecity project.

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Instructor

Daniel Gatica-Perez

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Related concepts (32)

Computer vision

Computer vision tasks include methods for , , and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images (the input to the retina in the human analog) into descriptions of the world that make sense to thought processes and can elicit appropriate action.

Image analysis

Image analysis or imagery analysis is the extraction of meaningful information from s; mainly from s by means of techniques. Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face. Computers are indispensable for the analysis of large amounts of data, for tasks that require complex computation, or for the extraction of quantitative information.

ImageNet

The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. ImageNet contains more than 20,000 categories, with a typical category, such as "balloon" or "strawberry", consisting of several hundred images.

Instagram

Instagram is a photo and video sharing social networking service owned by American company Meta Platforms. The app allows users to upload media that can be edited with filters, be organized by hashtags, and be associated with a location — via geographical tagging. Posts can be shared publicly or with preapproved followers. Users can browse other users' content by tags and locations, view trending content, like photos, and follow other users to add their content to a personal feed.

Computer stereo vision

Computer stereo vision is the extraction of 3D information from digital images, such as those obtained by a CCD camera. By comparing information about a scene from two vantage points, 3D information can be extracted by examining the relative positions of objects in the two panels. This is similar to the biological process of stereopsis. In traditional stereo vision, two cameras, displaced horizontally from one another, are used to obtain two differing views on a scene, in a manner similar to human binocular vision.

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