id: 05965143
dt: a
an: 05965143
au: Zhang, He; Augilius, Eimontas; Honkela, Timo; Laaksonen, Jorma; Gamper,
    Hannes; Alene, Henok
ti: Analyzing emotional semantics of abstract art using low-level image
    features.
so: Gama, João (ed.) et al., Advances in intelligent data analysis X. 10th
    international symposium, IDA 2011, Porto, Portugal, October 29‒31,
    2011. Proceedings. Berlin: Springer (ISBN 978-3-642-24799-6/pbk).
    Lecture Notes in Computer Science 7014, 413-423 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: emotional semantics; abstract art; psychophysical evaluation; image
    features; classification
ci: 
li: doi:10.1007/978-3-642-24800-9_38
ab: Summary: In this work, we study people’s emotions evoked by viewing
    abstract art images based on traditional low-level image features
    within a binary classification framework. Abstract art is used here
    instead of artistic or photographic images because those contain
    contextual information that influences the emotional assessment in a
    highly individual manner. Whether an image of a cat or a mountain
    elicits a negative or positive response is subjective. After discussing
    challenges concerning image emotional semantics research, we
    empirically demonstrate that the emotions triggered by viewing abstract
    art images can be predicted with reasonable accuracy by machine using a
    variety of low-level image descriptors such as color, shape, and
    texture. The abstract art dataset that we created for this work has
    been made downloadable to the public.
rv: