&lt;title&gt;Performance-scalable computational approach to main-subject detection in photographs&lt;/title&gt;

Luo, Jiebo; Etz, S.P.; Singhal, Amit; Gray, Robert T.

doi:10.1117/12.429521

Cited by 8 publications

(5 citation statements)

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“…In addition, images with people may have a different image structure than images without people [17]. Main subject size is an important attribute because it also dictates the structure of the image [18]. As an example, a closeup image of a child has very different characteristics than a wideangle scenic image.…”

Section: Introductionmentioning

confidence: 99%

Measuring the perceived aesthetic quality of photographic images

Cerosaletti

Loui

2009

2009 International Workshop on Quality of Multimedia Experience

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There have been few studies that empirically assess the perception of aesthetics in photography. This study addressed a few image attributes that were hypothesized to be important to the perception of aesthetics. Thirty consumer photographers evaluated 450 images in a repeated measures factorial experiment and provided artistic quality ratings on a 0 to 100-point scale. The image set balanced main subject size, images with people and without people, and the type of perspective cue. Three first-party scenes supplied by each observer were evaluated by all observers in the study. Study results indicate that aesthetic preferences were mediated by all three of the study variables as a significant threeway interaction. Principal component and cluster analyses provided further insight into other features that impact aesthetic preference. Images in this study formed a continuum of quality ranging from low aesthetic and low technical quality to high aesthetic and high technical quality.

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Section: Introductionmentioning

confidence: 99%

Measuring the perceived aesthetic quality of photographic images

Cerosaletti

Loui

2009

2009 International Workshop on Quality of Multimedia Experience

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“…The algorithms avoid any a priori training information as does the Bayes net approach to detect the main subject. 1,2 The algorithms also avoid the iterations or computationally intensity of the hierarchical wavelet transform. 3,4 The algorithms do not depend on the scene setting or content.…”

Section: Resultsmentioning

confidence: 99%

Unsupervised automation of photographic composition rules in digital still cameras

Banerjee

Evans

2004

SPIE Proceedings

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When taking pictures, professional photographers apply photographic composition rules, e.g. rule of thirds. The rule of thirds says to place the main subject's center at one of four places: at 1 3 or 2 3 of the picture width from left edge, and 1 3 or 2 3 of the picture height from the top edge. This paper develops low-complexity unsupervised methods for digital still cameras to 1 segment the main subject and 2 realize the rule-of-thirds. The main subject segmentation method uses the auto-focus lter, opens the shutter aperture fully, and segments the resulting image. These camera settings place the main subject in focus and blur the rest of the image by di used light. The segmentation utilizes the di erence in frequency content b e t w een the main subject and blurred background. The segmentation does not depend on prior knowledge of the indoor outdoor setting or scene content. The rule-of-thirds method moves the centroid of the main subject to the closest of the four rule-of-thirds locations. We rst de ne an objective function that measures how close the main subject placement obeys the rule-of-thirds, and then reposition the main subject in order to optimize the objective function. For multiple main subjects, the proposed algorithm could be extended to use rule-of-triangles by adding an appropriate constraint.

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“…In addition, images with people may have a different image structure than images without people [24]. Main subject size is an important attribute because it also dictates the structure of the image [25]. As an example, a close-up image of a child has very different characteristics than a wide angle scenic image.…”

Section: Selection Of Test Imagesmentioning

confidence: 99%