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For Aesthetic Workshop 2015, Download And Install Business Intelligence Tools
A feature paper can be an original research article, a major new research study that often uses several methods, or a comprehensive review paper with a concise and clear update on the latest advances in the field that examines the most exciting science literature. This paper suggests future research or practice.
Global scientific journal editors select Editor’s Choice articles. The editors choose a few recent journal articles they think readers will like or are relevant to each research area. To showcase the journal’s most exciting research.
Intelligent Systems and Data Mining Research Group, Albacete Research Institute of Informatics (I3A), UCLM, 02071 Albacete, Spain.
Received: October 8, 2021; Revised: October 18; Accepted: October 20; Published: October 23.
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Computer vision determines an image’s aesthetic quality. This helps social networks automate image processing. Aesthetic Selector is an application that can identify high-quality images, display decision information, and apply the best filters to enhance an image. To determine the filters for the Aesthetic Selector application, we analyzed the main aesthetic quality proposals and identified their strengths and weaknesses. The application performed well in image selection, image search, and filter selection. To better understand the model, we explored different visualization tools. These methods help model classification by identifying image areas of importance. The application includes this interpretability model.
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This work developed Aesthetic Selector, an image beauty evaluation app. The filter selects the best images in the input folder, and the explanation defines the image areas that affect classification. Combining filter material with other applications yields efficient results.
Deep learning solves aesthetic quality evaluation in this paper. This work mostly uses deep neural networks trained on more data for transfer learning [2]. The most popular deep neural network frameworks for computer vision are AlexNet [3], VGG [4], Inception [5], ResNet [6], and MobileNet [7], all of which originated from ImageNet research [8]. Transfer learning’s main goal is to use the network’s ImageNet knowledge, which can recognize 1000 concepts.
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Many recent articles and research works have presented unique tools to explore this inner workings. These tools provide more information as these models make predictions. This work’s main challenge is using these new techniques to assess aesthetic quality and integrating them into our application.
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