Studying aesthetics in photographic images using a computational approach

Abstract

The aesthetic quality of a picture is automatically inferred using visual content as a machine learning problem using, for example, a peer-rated, on-line photo sharing Website as data source. Certain visual features of images are extracted based on the intuition that they can discriminate between aesthetically pleasing and displeasing images. A one-dimensional support vector machine is used to identify features that have noticeable correlation with the community-based aesthetics ratings. Automated classifiers are constructed using the support vector machines and classification trees, with a simple feature selection heuristic being applied to eliminate irrelevant features. Linear regression on polynomial terms of the features is also applied to infer numerical aesthetics ratings.

Description

REFERENCE TO RELATED APPLICATION[0001]This application claims priority from U.S. Provisional Patent Application Ser. No. 60 / 916,467, filed May 7, 2007, the entire content of which is incorporated herein by reference.GOVERNMENT SPONSORSHIP[0002]This invention was made with government support under Grant No. 0347148, awarded by The National Science Foundation. The Government has certain rights in the invention.FIELD OF THE INVENTION[0003]This invention relates generally to digital image analysis and, in particular, to automatically inferring aesthetic quality of pictures based upon visual contentBACKGROUND OF THE INVENTION[0004]Photography is defined as the art or practice of taking and processing photographs. Aesthetics in photography is how people usually characterize beauty in this form of art. There are various ways in which aesthetics is defined by different people. There exists no single consensus on what it exactly pertains to. The broad idea is that photographic images that ar...

AI Technical Summary

Benefits of technology

[0014]The system and method offer numerous potential benefits. When low-level image features can be used to infer the aesthetics ratings that an image deserves, the result can be used by photographers to get a rough estimate of their shot composition quality, leading to adjustment in camera parameters or shot positioning for improved aesthetics. Camera manufacturers may incorporate a “suggested composition” feature into their products. Alternatively, a content-based image retrieval (CBIR) system can use the aesthetics score to discriminate between visually similar images, giving greater priority to more pleasing query results. Biologically speaking, a reasonable solution to this problem can lead to a better understanding of human vision.

Problems solved by technology

A professional photographer, on the other hand, may be wondering how difficult it may have been to take or to process a particular shot, the sharpness and the color contrast of the picture, or whether the “rules of thumb” in photography have been maintained.

All these issues make the measurement of aesthetics in pictures or photographs extremely subjective.

Hence, such ratings may be used as indicators of aesthetics in photography, but with a caveat: the nature of any peer-rated community is such that it leads to unfair judgments under certain circumstances, and Photo.net is no exception, making acquired data fairly noisy.

The originality score given to some photographs can also be hard to interpret, because what seems original to some viewers may not be so for others.

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