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008			230126s2022 sz | s |||| 0|eng d
020			_a9783030814656
024	7		_a10.1007/978-3-030-81465-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1650 _b2022 EB
245	0	0	_aHuman Perception of Visual Information : _bPsychological and Computational Perspectives _cedited by Bogdan Ionescu, Wilma A. Bainbridge, Naila Murray
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (IX, 292 páginas) _b69 ilustraciones, 56 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_aPreface -- Chapter 1 -- The Ingredients of Scenes That Affect Object Search and Perception -- Chapter 2 -- Exploring Deep Fusion Ensembling for Automatic Visual Interestingness Prediction -- Chapter 3 -- Affective Perception: The Power is in the Picture -- Chapter 4 -- Computational Emotion Analysis From Images: Recent Advances and Future Directions -- Chapter 5 -- The Interplay Of Objective And Subjective Factors In Empirical Aesthetics -- Chapter 6 -- Advances and Challenges in Computational Image Aesthetics -- Chapter 7 -- Shared Memories Driven by the Intrinsic Memorability of Items -- Chapter 8 -- Memorability: an Image-computable Measure of Information Utility -- Chapter 9 -- The Influence of Low -- and Mid-Level Visual Features on the Perception of Streetscape Qualities -- Chapter 10 -- Who Sees What? Examining Urban Impressions in Global South Cities.
520			_aRecent years have witnessed important advancements in our understanding of the psychological underpinnings of subjective properties of visual information, such as aesthetics, memorability, or induced emotions. Concurrently, computational models of objective visual properties such as semantic labelling and geometric relationships have made significant breakthroughs using the latest achievements in machine learning and large-scale data collection. There has also been limited but important work exploiting these breakthroughs to improve computational modelling of subjective visual properties. The time is ripe to explore how advances in both of these fields of study can be mutually enriching and lead to further progress. This book combines perspectives from psychology and machine learning to showcase a new, unified understanding of how images and videos influence high-level visual perception - particularly interestingness, affective values and emotions, aesthetic values, memorability, novelty, complexity, visual composition and stylistic attributes, and creativity. These human-based metrics are interesting for a very broad range of current applications, ranging from content retrieval and search, storytelling, to targeted advertising, education and learning, and content filtering. Work already exists in the literature that studies the psychological aspects of these notions or investigates potential correlations between two or more of these human concepts. Attempts at building computational models capable of predicting such notions can also be found, using state-of-the-art machine learning techniques. Nevertheless their performance proves that there is still room for improvement, as the tasks are by nature highly challenging and multifaceted, requiring thought on both the psychological implications of the human concepts, as well as their translation to machines.
988			_aSpringer_Computer_2022
650		7	_2embne _9152614 _aReconocimiento de formas
650		7	_2embne _9159793 _aVisión por ordenador
650		7	_2embne _9166090 _aAprendizaje automático
776	0	8	_iPrinted edition: _z9783030814649
776	0	8	_iPrinted edition: _z9783030814663
776	0	8	_iPrinted edition: _z9783030814670
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi-org.ezproxy.universidadeuropea.es/10.1007/978-3-030-81465-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eIG _zSI