Subjective Visual Attributes
Computer vision tasks such as automated object recognition or localization, which are related to the semantic content of images, are well-studied problems. Until recently however, it was unclear whether image properties such as aesthetic quality, which are experienced differently by each individual, were amenable to automatic description and categorization.
This debate has now been settled, with works demonstrating that image saliency, aesthetics, iconicity and memorability can to a great extent be predicted by training supervised models on visual data. Automatic prediction of such subjective visual attributes can enable technologies such as personalization of marketing materials, enhancing learning materials, and database management and compression.
However, several interrelated challenges exist for studying subjective image properties. Foremost is the need for rich annotations that can capture the range of subjective opinions that may exist for an image, as there is never total agreement among viewers about how aesthetically pleasing or memorable an image is. This need may in turn may require the acquisition and analysis of large-scale image datasets. In addition, the design of image representations that adequately capture image characteristics such as aesthetic quality or iconicity are active research domains.
Our group has made important contributions to visual analysis of subjective attributes, with a focus on image aesthetics. We have published a dataset of over 200K images with extensive annotations related to their aesthetic quality, including real-valued scores and textual comments given to each image by dozens of photography enthusiasts. We have also provided an extensive analysis of the informational content present in these annotations and explored the wide variety of applications they enable, including aesthetic quality assessment, image re-ranking, and style tagging. We also demonstrated that Fisher vectors (FVs), a state-of-the-art generic image representation developed in our group, can be effectively used to train models of image aesthetics. Our work on visual saliency has shown that FVs are also effective for learning image saliency models.
End-to-End Saliency Mapping via Probability Distribution Prediction, Saumya Jetley, Naila Murray, Eleonora Vig, CVPR 2016 (Las Vegas, USA)
Learning Beautiful (and ugly) attributes, Luca Marchesotti, Florent Perronnin, BMVC 2013 (Bristol, U.K.)
Learning to Rank Images using Semantic and Aesthetic Labels, Naila Murray, Luca Marchesotti, Florent Perronnin, BMVC 2012 (Guilford, U.K.)
AVA: A Large-Scale Database for Aesthetic Visual Analysis, Naila Murray, Luca Marchesotti, Florent Perronnin, CVPR 2012 (Providence, U.S.)
Towards automatic and flexible concept transfer, Naila Murray, Sandra Skaff, Luca Marchesotti, Florent Perronnin, Computers & Graphics 2012
Assessing the aesthetic quality of photographs using generic image descriptors, Luca Marchesotti, Florent Perronnin, Diane Larlus, Gabriela Csurka, ICCV 2011 (Barcelona, Spain)
Learning Moods and Emotions from Color Combinations, Luca Marchesotti, Gabriela Csurka, Craig Saunders, Sandra Skaff, ICVGIP 2011, (Honorable mention),(Chennai, India)
Font Retrieval on Large Scale: an experimental study, Luca Marchesotti, Florent Perronnin, Saurabh Kataria, ICIP 2010 (Hong Kong)
A framework for visual saliency detection with applications to image thumbnailing, Luca Marchesotti, Claudio Cifarelli, Gabriela Csurka, ICCV 2009 (Kyoto, Japan)