scholarly journals Do happy faces really modulate liking for Jackson Pollock art and statistical fractal noise images?

Psihologija ◽  
2017 ◽  
Vol 50 (3) ◽  
pp. 219-237
Author(s):  
Katrin Mundloch ◽  
Marie Winterberg ◽  
Wanja Hemmerich ◽  
Philipp Holzwig ◽  
Anna Rupanova ◽  
...  
Keyword(s):  
Visual Stimuli ◽  
Image Features ◽  
Affective Priming ◽  
Abstract Art ◽  
Jackson Pollock ◽  
Visual Material ◽  
Low Level ◽  
Prime Condition ◽  
Fractal Noise

Flexas et al. (2013) demonstrated that happy faces increase preference for abstract art if seen in short succession. We could not replicate their findings. In our first experiment, we tested whether valence, saliency or arousal of facial primes can modulate liking of Jackson Pollock art crops. In the second experiment, the emphasis was on testing another type of abstract visual stimuli which possess similar low-level image features: statistical fractal noise images. Pollock crops were rated significantly higher when primed with happy faces in contrast to neutral faces, but not differently to the no-prime condition. Findings of our study suggest that affective priming with happy faces may be stimulus-specific and may have inadvertent effects on other abstract visual material.

scholarly journals Analyzing Emotional Semantics of Abstract Art Using Low-Level Image Features

2011 ◽  
pp. 413-423 ◽  
Author(s):  
He Zhang ◽  
Eimontas Augilius ◽  
Timo Honkela ◽  
Jorma Laaksonen ◽  
Hannes Gamper ◽  
...  
Keyword(s):  
Image Features ◽  
Abstract Art ◽  
Low Level

Machine Learning Approach for Content Based Image Retrieval

2012 ◽  
pp. 1-11 ◽  
Author(s):  
Siddhivinayak Kulkarni
Keyword(s):  
Neural Networks ◽  
Image Retrieval ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Learning Approach ◽  
Query Image ◽  
Low Level ◽  
Machine Learning Approach ◽  
Efficient Retrieval

Developments in technology and the Internet have led to an increase in number of digital images and videos. Thousands of images are added to WWW every day. Content based Image Retrieval (CBIR) system typically consists of a query example image, given by the user as an input, from which low-level image features are extracted. These low level image features are used to find images in the database which are most similar to the query image and ranked according their similarity. This chapter evaluates various CBIR techniques based on fuzzy logic and neural networks and proposes a novel fuzzy approach to classify the colour images based on their content, to pose a query in terms of natural language and fuse the queries based on neural networks for fast and efficient retrieval. A number of experiments were conducted for classification, and retrieval of images on sets of images and promising results were obtained.

Toward Semantically Meaningful Feature Spaces for Efficient Indexing in Large Image Databases

2011 ◽  
pp. 1-29
Author(s):  
Anne H.H. Ngu ◽  
Jialie Shen ◽  
John Shepherd
Keyword(s):  
Distance Function ◽  
Image Features ◽  
Image Databases ◽  
Multimedia Databases ◽  
Visual Features ◽  
Hybrid Architecture ◽  
Low Level ◽  
Feature Vectors ◽  
Training Cost ◽  
Low Dimensional

The optimized distance-based access methods currently available for multimedia databases are based on two major assumptions: a suitable distance function is known a priori, and the dimensionality of image features is low. The standard approach to building image databases is to represent images via vectors based on low-level visual features and make retrieval based on these vectors. However, due to the large gap between the semantic notions and low-level visual content, it is extremely difficult to define a distance function that accurately captures the similarity of images as perceived by humans. Furthermore, popular dimension reduction methods suffer from either the inability to capture the nonlinear correlations among raw data or very expensive training cost. To address the problems, in this chapter we introduce a new indexing technique called Combining Multiple Visual Features (CMVF) that integrates multiple visual features to get better query effectiveness. Our approach is able to produce low-dimensional image feature vectors that include not only low-level visual properties but also high-level semantic properties. The hybrid architecture can produce feature vectors that capture the salient properties of images yet are small enough to allow the use of existing high-dimensional indexing methods to provide efficient and effective retrieval.

A Method of Image Affective Semantic Classification Based on Attribution Reduction

2014 ◽  
Vol 556-562 ◽  
pp. 4820-4824
Author(s):  
Ying Xia ◽  
Le Mi ◽  
Hae Young Bae
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Classification Accuracy ◽  
Selection Process ◽  
Attribute Reduction ◽  
Image Features ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Semantic Classification ◽  
Low Level

In study of image affective semantic classification, one problem is the low classification accuracy caused by low-level redundant features. To eliminate the redundancy, a novel image affective classification method based on attributes reduction is proposed. In this method, a decision table is built from the extraction of image features first. And then valid low-level features are determined through the feature selection process using the rough set attribute reduction algorithm. Finally, the semantic recognition is done using SVM. Experiment results show that the proposed method improves the accuracy in image affective semantic classification significantly.

scholarly journals Taste in Art—Exposure to Histological Stains Shapes Abstract Art Preferences

i-Perception ◽  
2017 ◽  
Vol 8 (5) ◽  
pp. 204166951773607 ◽  
Author(s):  
Antonia M. Böthig ◽  
Gregor U. Hayn-Leichsenring
Keyword(s):  
Art History ◽  
Visual Stimuli ◽  
Abstract Art ◽  
Exposure Effect ◽  
Domain Independent

Exposure to art increases the appreciation of artworks. Here, we showed that this effect is domain independent. After viewing images of histological stains in a lecture, ratings increased for restricted subsets of abstract art images. In contrast, a lecture on art history generally enhanced ratings for all art images presented, while a lecture on town history without any visual stimuli did not increase the ratings. Therefore, we found a domain-independent exposure effect of images of histological stains to particular abstract paintings. This finding suggests that the ‘taste’ for abstract art is altered by visual impressions that are presented outside of an artistic context.

scholarly journals The role of low-level image features in the affective categorization of rapidly presented scenes

PLoS ONE ◽  
2019 ◽  
Vol 14 (5) ◽  
pp. e0215975 ◽  
Author(s):  
L. Jack Rhodes ◽  
Matthew Ríos ◽  
Jacob Williams ◽  
Gonzalo Quiñones ◽  
Prahalada K. Rao ◽  
...  
Keyword(s):  
Image Features ◽  
Low Level
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