000063663 001__ 63663 000063663 005__ 20171221155148.0 000063663 037__ $$aTAZ-TFM-2017-879 000063663 041__ $$aeng 000063663 1001_ $$aLagunas Arto, Manuel 000063663 24200 $$aA Computational Model for Icons Appearance 000063663 24500 $$aModelo Computacional de Apariencia de Iconos 000063663 260__ $$aZaragoza$$bUniversidad de Zaragoza$$c2017 000063663 506__ $$aby-nc-sa$$bCreative Commons$$c3.0$$uhttp://creativecommons.org/licenses/by-nc-sa/3.0/ 000063663 520__ $$aNowadays, visual design and interaction are playing an important role in traditional marketing pipelines. Within the context of visual design, the proper selection of icons can be the key of success for engaging and captivating the audience. In this work, we present a method based on a Convolutional Neural Network to obtain icon sets optimized for the properties of style and visual similarity and ease the process of selection to the users. We also obtain a new dataset of icons using an online database where the icons are uploaded by graphic designers in small collections that usually share same semantic meaning and appearance. Since the dataset is labelled by the designers, we cannot be completely sure that there is noise or non-reliable points in the collections. Thus, we use part of the raw data from this dataset to train the CNN, and the rest to collect human-rated information which used for testing. The final goal of the method is to train a Convolutional Neural Network to map each input image to a new Euclidean space where the properties of style and visual similarity are considered. To do so, we train the model using a triplet loss which ensures that images sharing style and visually similar remain closer in the new feature space, while keeps farther icons without similar appearance properties. At the end, we present several results and applications that can be helpful for designers and non-expert users while exploring large collections of icons. 000063663 521__ $$aMáster Universitario en Modelización e Investigación Matemática, Estadística y Computación 000063663 540__ $$aDerechos regulados por licencia Creative Commons 000063663 700__ $$aGarcés García, Elena$$edir. 000063663 700__ $$aGutiérrez Pérez, Diego$$edir. 000063663 7102_ $$aUniversidad de Zaragoza$$bMatemática Aplicada$$cMatemática Aplicada 000063663 8560_ $$f504185@celes.unizar.es 000063663 8564_ $$s8233264$$uhttps://zaguan.unizar.es/record/63663/files/TAZ-TFM-2017-879.pdf$$yMemoria (eng) 000063663 909CO $$ooai:zaguan.unizar.es:63663$$pdriver$$ptrabajos-fin-master 000063663 950__ $$a 000063663 951__ $$adeposita:2017-12-21 000063663 980__ $$aTAZ$$bTFM$$cCIEN