Aesthetic Visual Analysis (AVA) contains over 250,000 images along with a rich variety of meta-data including a large number of aesthetic scores for each image, semantic labels for over 60 categories as well as labels related to photographic style for high-level image quality categorization.

The last stream within the NESP 5.5 project was related to the conduct of an online survey to get aesthetic ratings of additional 3500 images downloaded from Flickr to improve the Artificial Intelligence (AI)-based system recognising and assessing the beauty of natural scenes, which had been developed in the previous NESP 3.2.3 project. Despite some earlier investment into this research area, there is still a need to improve the tools we use to measure the aesthetic beauty of marine landscapes. This research drew on images publicly available on the Internet (in particular through the photo sharing site Flickr) to build a large dataset of GBR images for the assessment of aesthetic value. Building on earlier work in NESP TWQ Hub Project 3.2.3, we conducted a survey focused on collecting beauty scores of an additional large number of GBR images (n = 3500).
This dataset consists of one dataset report, two word files and one excel file demonstrating the aesthetic ratings collected used to improve the accuracy of the aesthetic monitoring AI system.



Methods:
The third research stream was conducted on the basis of an online survey to collect aesthetic ratings of 1585 Australians to rate the aesthetic beauty of 3500 GBR underwater pictures downloaded and selected from Flickr. Flickr is an image hosting service and one of the main sources of images for our project. As per our requirement, we downloaded all images and their metadata (including coordinates where available) based on keyword filter such as “Great Barrier Reef”. The Flickr API is available for non-commercial (but commercial use is possible by prior arrangement) use by outside developers. To ensure a much larger and diverse supply of photographs, we have developed a python-based application using Flickr API that allowed us to download Flickr images by keyword (e.g. “Great Barrier Reef” available at https://www.flickr.com). The focus of this research was on under-water images, which had to be filtered from the downloaded Flickr photos. From the collected images we identified an additional number of 3020 relevant images with coral and fish contents out of a total of approximately 55,000 downloaded images. Matt Curnock, CSIRO expert, also provide 100 images from his private images taken at the GBR and consent to use these images for our research. In total, 3120 images were selected and renamed to be rated in a survey by Australian participants (see two file “Image modification” and “Matt image rename” in the AI folder for further details).

The survey was created on Qualtrics website and launched in in April 2020 using Qualtrics survey service. After giving the consent to participating in the online survey, each respondent was randomly exposed to 50 images of the GBR and rate the aesthetic of the GBR scenery on a 10 point scale (1-Very ugly/unpleasant – Very beautiful/pleasant). In total, 1585 complete and valid questionnaires were recorded. Aesthetic rating results was exported to an Excel file and used for improving the accuracy of the computer algorithm recognising and assessing the beauty of natural scenes which had been developed in the previous NESP 3.2.3 project.

Further information can be found here:
Stantic, B. and Mandal, R. (2020) Aesthetic Assessment of the Great Barrier Reef using Deep Learning. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns (30pp.). Available at https://nesptropical.edu.au/wp-content/uploads/2020/11/NESP-TWQ-Project-5.5-Technical-Report-3.pdf



Format:
The AI DATASET has one dataset report, one excel file showing aesthetic ratings of all images and two Word files showing how images downloaded from Flickr website and provided by Matt Curnock (CSIRO) were renamed and used for aesthetic ratings and AI development. The aesthetic rating results were later used to improve the accuracy of the AI aesthetic monitoring system for the GBR.

Further information can be found here:
Stantic, B. and Mandal, R. (2020) Aesthetic Assessment of the Great Barrier Reef using Deep Learning. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns (30pp.). Available at https://nesptropical.edu.au/wp-content/uploads/2020/11/NESP-TWQ-Project-5.5-Technical-Report-3.pdf



References:
Murray, N., Marchesotti, M. & Perronnin, F (2012). AVA: A Large-Scale Database for Aesthetic Visual Analysis. Available (09/10/17) http://refbase.cvc.uab.es/files/MMP2012a.pdf


Data Location:
This dataset is filed in the eAtlas enduring data repository at: data\custodian\2019-2022-NESP-TWQ-5\5.5_Measuring-aesthetics

The second stream within the NESP 5.5 project was conducted using eye-tracking technology to examine possible differences between three participant groups in evaluating the aesthetic beauty of GBR underwater sceneries. This research continue the efforts initiated in the previous NESP 3.2.3 project to explore the power of eye-tracking as an objective measure of human aesthetic assessment of GBR underwater sceneries. By employing a sample of three social-cultural groups (non-indigenous Australians, Chinese and First Peoples), this research provides further empirical evidence for the effectiveness of eye-tracking in aesthetic research in a cross-cultural context. Data collected using eye tracking was stored in one Excel file of eye-tracking data exported from Tobii eye-tracking device and 20 heatmaps showing participants’ visual attention to 20 images of underwater GBR sceneries.

Methods:

Following the initial research conducted in the previous NESP 3.2.3 project, 93 participants of various socio-cultural backgrounds (non-indigenous Australians, First People Australians and Chinese) were recruited using convenience sampling in this study. Participants were asked to sit in front of a screen-based eye-tracking equipment (i.e. Tobii T60 eye-tracker) after providing informed consent. Participants were free to look at each picture on screen as long as they wanted during which their eye movements were recorded (similar to lab setting in NESP 3.2.3). They also rated each picture on a 10-point beauty scale (1-Not beautiful at all, 10-Very beautiful). Raw eye-tracking data was then imported to IBM SPSS using SAV. format for data analysis. Raw eye-tracking data was then extracted from Tobii eye-tracking device (i.e. picture beauty, time to first fixation, fixation count, fixation duration and total visit time) in Exel format. Twenty heatmaps in Png format generated from the eye-tracking software to show participants’ visual attention were also included. As an extension of the previous study conducted within the NESP 3.2.3 project, data collected was used to examine the influences of social-cultural differences in aesthetic assessment of GBR underwater sceneries.

Advanced technologies were used in combination with self-reporting measurements for a better understanding of socio-cultural differences and socio-cultural influences on aesthetic assessment among three groups. Eye-tracking provides a measure of visual attention, enabling researchers to explore further potential differences among three groups regarding their interest in viewing and assessing the GBR aesthetics. Previous research (NESP 3.2.3) demonstrated that eye-tracking measures of viewers' visual attention (i.e., fixation duration and fixation count) and aesthetic ratings are correlated, suggesting the usefulness of eye-tracking in aesthetic research. This study verifies the usefulness of eye-tracking in aesthetic research in a cross-cultural context. Participants were exposed to 20 images of underwater GBR scenery in random order which were used in the previous focus groups.

Further information can be found in the following publication: Le, D., Becken, S., & Whitford, M. (2020) A cross-cultural investigation of the great barrier Reef aesthetics using eye-tracking and face-reader technologies. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns. Published online at https://nesptropical.edu.au/wp-content/uploads/2020/09/NESP-TWQ-Project-5.5-Technical-Report-2.pdf

Format:

The eye-tracking folder contains one Excel file containing raw eye-tracking data and 20 heatmaps generated from eye-tracking software in Png format.

Data Dictionary:

• Beauty1: Name of the corresponding GBR underwater picture used in the eye-tracking experiment
• ABeauty1: Aesthetic evaluations of the corresponding picture (e.g., Beauty1)
• EBeauty1: Aesthetic emotion (i.e., pleasant) of the corresponding picture (e.g., Beauty1)
• FD_Beauty1: Fixation duration in the picture Beauty1 (i.e. the average length of all fixations during all recordings in the whole picture). A longer fixation means that the object is more engaging in some way. Measurement unit:
• AOIFD_Beauty1: Fixation duration in the central area of interest (AOI) in picture Beauty1 (i.e. the average length of all fixations during all recordings in the whole picture). A longer fixation means that the object is more engaging in some way.
• FC_Beauty1: Fixation count in the picture Beauty1 (i.e. the average number of fixations in the picture).
• AOIFC_Beauty1: Fixation count in the central area of interest (AOI) in picture Beauty1 (i.e. the average number of fixations in the picture).

Similar labels are used for other pictures, including Beauty 2,3,4; Human 1,3,5,6; Medium 1,2,3,4; Restoration 1,2,3,8 and Ugly 1,2,3,4.

Further information can be found in the following publication: Le, D., Becken, S., & Whitford, M. (2020) A cross-cultural investigation of the great barrier Reef aesthetics using eye-tracking and face-reader technologies. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns. Published online at https://nesptropical.edu.au/wp-content/uploads/2020/09/NESP-TWQ-Project-5.5-Technical-Report-2.pdf

References:

Murray, N., Marchesotti, M. & Perronnin, F (2012). AVA: A Large-Scale Database for Aesthetic Visual Analysis. Available (09/10/17) http://refbase.cvc.uab.es/files/MMP2012a.pdf

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2019-2022-NESP-TWQ-5\5.5_Measuring-aesthetics