Dating Historical Color Images
By Frank Palermo, James Hays, Alexei A. Efros. 2012
117 citations recorded by the source corpus.
We introduce the task of automatically estimating the age of historical color photographs. We suggest features which attempt to capture temporally discriminative information based on the evolution of color imaging processes over time and evaluate the performance of both these novel features and existing features commonly utilized in other problem domains on a novel historical image data set. For the challenging classification task of sorting historical color images into the decade during which they were photographed, we demonstrate significantly greater accuracy than that shown by untrained humans on the same data set. Additionally, we apply the concept of data-driven camera response function estimation to historical color imagery, demonstrating its relevance to both the age estimation task and the popular application of imitating the appearance of vintage color photography.
Subfields: Computer Vision and Pattern Recognition
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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Trevor Darrell
- Li Fei-Fei
- Xiaogang Wang
- Dacheng Tao
- Jitendra Malik
- Ming-Hsuan Yang
- Abhinav Kumar Gupta
Citations in the corpus
- Recognizing Image Style (2013, background): “[24], who showed this to be the best performing single feature for determining the date of historical color images.”
- Learning Everything about Anything: Webly-Supervised Visual Concept Learning (2014, background): “This can help in not only learning the evolution of a concept [26], but also in automatically dating detected instances [35].”
- Runway to Realway: Visual Analysis of Fashion (2015, background): “These later tasks, predicting year and season for clothing, dovetail with recent work trying to predict when a historical photo was taken based on its distribution of colors [19], or identifying the year or make of a car from discriminative image patches [16].”
- Visual7W: Grounded Question Answering in Images (2015, background): “The 7W questions roughly correspond to an array of standard vision tasks: what [9, 15, 39], where [24, 50], when [30, 32], who [35, 42], why [33], how [23, 31] and which [16, 17].”