Image Aesthetic Ranking For Recommendation And Evaluation
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2021-04-10
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Abstract
Aesthetics is a branch of philosophy science that deals with the study of emotions in relation to the concept of aesthetics .
Choose favorite image from a group of collections is boring and time consuming work , as users have to browse the image collections, choose the desired images and remove the unwanted images. Developing recommendation system that uses a hierarchical clustering algorithm to find dominant images, making it easier for users to select images with the highest aesthetic quality. The aesthetic features of the image are extracted and the images are classification . This thesis presents a method to achieve the aesthetic quality of the images captured by continuous shooting mode. The continuous shooting mode is to capture a group of images with one click of a button in a very short time. In order to help the user choose the best shot without making any effort and costs, we suggested two ways to distinguish high-quality images from Low-quality images, the first method is to extract aesthetic features by the proposed algorithm, the aesthetic feature extraction algorithm , and image classification by image classifier (Support Vector Machine) and we obtained accurate results, the second method is using a convolutional neural network (AlexNet) to extract features and classify images. Experimental results showed that the two methods achieved good results in assessing the aesthetic quality of images. The dataset used in our method is the AVA DPchallenge portal, which is a very large database that contains a large number of categories. The selection of images is based on the voting rate, which ranges from 1 to 10, where images with a rating of less than 5 images are considered low aesthetic and images that contain On the average vote higher or equal 5 aesthetic high. Accuracy of proposed system equal 98% for both methods , the The supported programming language is MATLAB R2018b.