Seeing the Intangible: Surveying Automatic High-Level Visual Understanding from Still Images
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The field of Computer Vision (CV) was born with the single grand goal of complete image understanding: providing a complete semantic interpretation of an input image. What exactly this goal entails is not immediately straightforward, but theoretical hierarchies of visual understanding point towards a top level of full semantics, within which sits the most complex and subjective information humans can detect from visual data. In particular, non-concrete concepts including emotions, social values and ideologies seem to be protagonists of this "high-level" visual semantic understanding. While such "abstract concepts" are critical tools for image management and retrieval, their automatic recognition is still a challenge, exactly because they rest at the top of the "semantic pyramid": the well-known semantic gap problem is worsened given their lack of unique perceptual referents, and their reliance on more unspecific features than concrete concepts. Given that there seems to be very scarce explicit work within CV on the task of abstract social concept (ASC) detection, and that many recent works seem to discuss similar non-concrete entities by using different terminology, in this survey we provide a systematic review of CV work that explicitly or implicitly approaches the problem of abstract (specifically social) concept detection from still images. Specifically, this survey performs and provides: (1) A study and clustering of high level visual understanding semantic elements from a multidisciplinary perspective (computer science, visual studies, and cognitive perspectives); (2) A study and clustering of high level visual understanding computer vision tasks dealing with the identified semantic elements, so as to identify current CV work that implicitly deals with AC detection.
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