Naturalness and interestingness of test images for visual quality evaluation

Halonen, Raisa; Westman, Stina; Oittinen, Pirkko

doi:10.1117/12.872390

Cited by 17 publications

(19 citation statements)

References 33 publications

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“…The attractiveness [10] and aesthetics [11,16] of images retrieved always play an important role in user's satisfaction. Lerman et al [20] for example, propose an automatic method to assign photo attractiveness values to photos by using textual and visual features.…”

Section: Related Workmentioning

confidence: 99%

Image ranking based on user browsing behavior

Trevisiol

Chiarandini

Aiello

et al. 2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Ranking of images is difficult because many factors determine their importance (e.g., popularity, quality, entertainment value, context, etc.). In social media platforms, ranking also depends on social interactions and on the visibility of the images both inside and outside those platforms. In this context, the application of standard ranking methods is not clearly understood, and neither are the subtleties associated with taking into account social interaction, internal, and external factors. In this paper, we use a large Flickr dataset and investigate these factors by performing an in-depth analysis of several ranking algorithms using both internal (i.e., within Flickr) and external (i.e., links from outside of Flickr) factors. We analyze rankings given by common metrics used in image retrieval (e.g., number of favorites), and compare them with metrics based on page views (e.g., time spent, number of views). In addition, we represent users' navigation by a graph and combine session models with some of these metrics, comparing with PageRank and BrowseRank. Our experiments show significant differences between the rankings, providing insights on the impact of social interactions, internal, and external factors in image ranking.

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Section: Related Workmentioning

confidence: 99%

Image ranking based on user browsing behavior

Trevisiol

Chiarandini

Aiello

et al. 2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…[31][32][33] In order to better imitate the preference manner of HVS, the pairwise comparison methodology has been widely used in subjective quality evaluation. [31][32][33] In order to better imitate the preference manner of HVS, the pairwise comparison methodology has been widely used in subjective quality evaluation.…”

Section: Pairwise Comparisonmentioning

confidence: 99%

“…In the standard subjective studies for visual comfort, each observer is asked to assign each image a number within a specific range, e.g., 1 to 5, reflecting a specific degree of experienced visual discomfort. [31][32][33] After constructing the PSIP set with reliable preference labels, the task of VCA can be simplified as a preference classification problem. Although these metrics seem rather effective for VCA, they still suffer from the following limitations.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional visual comfort assessment via preference learning

Jiang

Shao

Jiang

et al. 2015

J. Electron. Imaging

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Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/13/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Abstract. Three-dimensional (3-D) visual comfort assessment (VCA) is a particularly important and challenging topic, which involves automatically predicting the degree of visual comfort in line with human subjective judgment. State-of-the-art VCA models typically focus on minimizing the distance between predicted visual comfort scores and subjective mean opinion scores (MOSs) by training a regression model. However, obtaining precise MOSs is often expensive and time-consuming, which greatly constrains the extension of existing MOS-aware VCA models. This study is inspired by the fact that humans tend to conduct a preference judgment between two stereoscopic images in terms of visual comfort. We propose to train a robust VCA model on a set of preference labels instead of MOSs. The preference label, representing the relative visual comfort of preference stereoscopic image pairs (PSIPs), is generally precise and can be obtained at much lower cost compared with MOS. More specifically, some representative stereoscopic images are first selected to generate the PSIP training set. Then, we use a support vector machine to learn a preference classification model by taking a differential feature vector and the corresponding preference label of each PSIP as input. Finally, given a testing sample, by considering a full-round paired comparison with all the selected representative stereoscopic images, the visual comfort score can be estimated via a simple linear mapping strategy. Experimental results on our newly built 3-D image database demonstrate that the proposed method can achieve a better performance compared with the models trained on MOSs.

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“…Motivated by the affective dimension of interest and the previous findings on interestingness in images [18,10], we labeled GIFs on interestingness, aesthetics, arousal and valence (pleasantness) on five point scales. We also labeled them on the presence of explicit or erotic content due to the strong effect of sexual arousal in emotional responses [3].…”

Section: Datasetmentioning

confidence: 99%

“…Silvia among other psychologist posited that interest is an emotion which has consistent subjective feelings and expressions in adults [15]. Halonen et al [10] identified a set of characteristics that are related to visual interestingness through a qualitative study. The characteristics which were deemed relevant were aesthetics, affect, colors, composition, genre and personal connection.…”

Section: Introductionmentioning

confidence: 99%

Analyzing and Predicting GIF Interestingness

Gygli

Soleymani

2016

Proceedings of the 24th ACM International Conference on Multimedia

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Animated GIFs have regained huge popularity. They are used in instant messaging, online journalism, social media, among others. In this paper, we present an in-depth study on the interestingness of GIFs. We create and annotate a dataset with a set of affective labels, which allows us to investigate the sources of interest. We show that GIFs of pets are considered more interesting that GIFs of people. Furthermore, we study the connection of interest to other features and factors such as popularity. Finally, we build a predictive model and show that it can estimate GIF interestingness with high accuracy. Our model outperforms the existing methods on GIF popularity, as well as a model based on still image interestingness, by a large margin. We envision that the insights and method developed can be used for automatic recognition and generation of interesting GIFs.

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Naturalness and interestingness of test images for visual quality evaluation

Cited by 17 publications

References 33 publications

Image ranking based on user browsing behavior

Image ranking based on user browsing behavior

Three-dimensional visual comfort assessment via preference learning

Analyzing and Predicting GIF Interestingness

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