Visualization of 23,581 photos taken during 24 hours in Brooklyn area. See http://phototrails.net/instagram-cities/
We are a digital humanities research lab (www.softwarestudies.com) at University of California, San Diego (UCSD) and The Graduate Center, City University of New York (CUNY). We are working on analyzing and visualizing cultural patterns in large sets of images and video. The examples of our projects include analysis/visualization of 2.3 million Instagram photos, 1 million manga pages, and all paintings of Vincent van Gogh.
We started this research in 2007, and we call it cultural analytics.
We are looking for a Computer Vision researcher to join us (freelance working remotely, or full-time).
You can live anywhere (as long as you have US social security number) and work freelance, choosing your own hours. So if you already have a full-time position, but have some free time, this job is for you. (Email, Skype or Google Hangout - we love them all). This freelance position is available for up to 1 year.
Or, if you happen to live in San Diego (where our lab is located) and prefer to work full-time (with benefits), this position is available for up to 2 years.
So far, we only used low-level visual features in our projects, and we want to go further - with your help:
You will use appropriate computer vision techniques to analyze visual characteristics and content of large sets of user-captured photographs from media sharing sites. Specifically:
1) You will use state of the art scene classification methods to automatically classify large sets of photographs from media sharing sites. [1 - See examples below]
2) Photo classification for a few selected object types that can be identified with high accuracy (such as faces / figures). [2 - see reference for current state of the art in objects classification task].
3) Analysis of visual attributes such as color, lines, shapes, composition. (We will use them not only as input to (2) and (3) but also for visualizing visual patterns over time, and the differences between image sets.)
In addition to photographs, you will also work on analyzing cultural images such as paintings, comics, or magazine covers and pages. The goal is to implement features which can capture stylistic evolution in image sets (for , all works by an artist, or all pages in a magazine over a number of years.) [3 - see examples below]
You can use any software (OpenCV, Matlab, etc.).
The analysis does not need to run in real-time.
Some photo sets may have EXIF data and other capture metadata; others may have only titles and tags; still others may only have even less data associated with them.
Note that our goal is not to come up with new or best possible algorithms - instead, we want to apply existing algorithms on large sets of images (and possibly video) to find out interesting things about culture (patterns in photos shared online, evolution of artists, etc.)
We are flexible and open - there are lots of cultural image sets waiting to be analyzed. The idea is to take well-performing computational methods and apply to them to image sets where we can get interesting results. We don't have particular narrow "problems" which need to be solved - instead, we want to explore patterns in any interesting cultural image set where computer methods can produce results.
Our work is presented on the web (see, for example, Phototrais web site), in intenational exhibitions, and in articles and book chapters. Your name will appear on all our outputs which use your work. If you want to publish technical paper(s) about the research done with our lab, you can be the first author on these publications.
Here are examples of media coverage of our most recent project Phototrails:
Wired: Using 2 Million Instagram Pics to Map a City’s Visual Signature.
Fast.Company Co.Create: See Your City's Unique Visual Signature, Created by its Intagram Photos.
Creators Project: What Do Your Instagram Photos Say About Your City?
The Atlantic Cities: The Visual Signature of Your City.
The Guardian: San Francisco viewed through Instagram photos.
Qualifications - Required:
1) PhD in computer vision, image processing, machine learning, or related fields (MA considered).
2) Record of computer science publications in scene classification and/or object recognition in photographs.
1) Experience with analyzing art images / aesthetic features of photographs, including implementing high-level "artistic features" (composition, etc.)
2) Experience with analyzing big image sets (millions of images).
3) Experience with automatic video analysis.
4) Experience with image collection visualization.
Open - depends on your experience and demonstrated results in relevant areas.
Send email with your CV, publications and projects links, salary requirements and availability (starting date, number of hours per week) to: email@example.com.
The review of applications begins now (July 25, 2013), and the offer will be made as soon as we find the right person.
 Examples of scene classification research:
Automatic Context Analysis for Image Classification and Retrieval.
Photo Classification by Integrating Image Content and Camera Metadata.
 Examples of analyzing art images / aesthetic features of photographs:
Affective Image Classification using Features Inspired by Psychology and Art Theory.
Studying Aesthetics in Photographic Images Using a Computational Approach.
 State of the art in object classification in photographs:
Visual Object Classes Challenge 2012 (VOC2012).