We present an approach for identifying picturesque highlights from large amounts of egocentric video data. Given a set of egocentric videos captured over the course of a vacation, our method analyzes the videos and looks for images that have good picturesque and artistic properties. We introduce novel techniques to automatically determine aesthetic features such as composition, symmetry and color vibrancy in egocentric videos and rank the video frames based on their photographic qualities to generate highlights. Our approach also uses contextual information such as GPS, when available, to assess the relative importance of each geographic location where the vacation videos were shot. Furthermore, we specifically leverage the properties of egocentric videos to improve our highlight detection. We demonstrate results on a new egocentric vacation dataset which includes 26.5 hours of videos taken over a 14 day vacation that spans many famous tourist destinations and also provide results from a user-study to access our results.
The other approach for vacation highlights is to look at the image aesthetics. These include high-level semantic features based on photography techniques @cite_4 , finding good composition for graphics image of a 3D object @cite_42 and cropping and retargeting based on an evaluation of the composition of the image like the rule-of-thirds, diagonal dominance and visual balance @cite_14 . We took inspiration from such approaches and developed novel algorithms to detect composition, symmetry and color vibrancy for egocentric videos.
@cite_14 @cite_42 @cite_4
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Aesthetic images evoke an emotional response that transcends mere visual appreciation. In this work we develop a novel computational means for evaluating the composition aesthetics of a given image based on measuring several well-grounded composition guidelines. A compound operator of crop-and-retarget is employed to change the relative position of salient regions in the image and thus to modify the composition aesthetics of the image. We propose an optimization method for automatically producing a maximally-aesthetic version of the input image. We validate the performance of the method and show its effectiveness in a variety of experiments. Altering the viewing parameters of a 3D object results in computer graphics images of varying quality. One aspect of image quality is the composition of the image. While the esthetic properties of an image are subjective, some heuristics used by artists to create images can be approximated quantitatively. We present an algorithm based on heuristic compositional rules for finding the format, viewpoint, and layout for an image of a 3D object. Our system computes viewing parameters automatically or allows a user to explicitly manipulate them. Traditionally, distinguishing between high quality professional photos and low quality amateurish photos is a human task. To automatically assess the quality of a photo that is consistent with humans perception is a challenging topic in computer vision. Various differences exist between photos taken by professionals and amateurs because of the use of photography techniques. Previous methods mainly use features extracted from the entire image. In this paper, based on professional photography techniques, we first extract the subject region from a photo, and then formulate a number of high-level semantic features based on this subject and background division. We test our features on a large and diverse photo database, and compare our method with the state of the art. Our method performs significantly better with a classification rate of 93 versus 72 by the best existing method. In addition, we conduct the first study on high-level video quality assessment. Our system achieves a precision of over 95 in a reasonable recall rate for both photo and video assessments. We also show excellent application results in web image search re-ranking.