20.500.12556/RUL-98939 Razpoznavanje hrane na podlagi slik z nevronskimi mrežami Image-based food recognition using neural networks V magistrski nalogi naslavljamo problem klasifikacije slik in sestave opisov slik s tremi implementiranimi metodami z nevronskimi mrežami (klasifikacija hrane, sestava opisov hrane in sestava opisov hrane z regijami), ki so bile učene in testirane na dveh podatkovnih zbirkah. Prva razdeljena na 21 kategorij hrane z 1470 slikami in druga, podatkovna zbirka opisov (2 kategoriji in 5 opisnih stavkov na sliko). Prva implementirana metoda—metoda klasifikacije hrane—uporablja arhitekturo GoogLeNet-Inception-v3 mreže (že učena na zbirki ILSVRC), ki je bila dodatno učena na naši podatkovni zbirki hrane na kateri dosega 82.4% top-1 in 98% top-5 točnosti. Druga metoda—metoda sestave opisov—uporablja arhitekturo Show and Tell mreže, ki je inicializirana z našim modelom klasifikacije hrane in doseže 23.3 točk perpleksnosti. Metoda ne sestavi popolnih opisov slik, ko sta prisotna dva ali več objekta na sliki, zato smo implementirali še metodo, ki bi izpisala vsebovanost objektov v slikah. Tretja metoda—metoda za sestavo opisov slik z regijami—uporablja isti vizualni model, ki je uporabljen v prejšnjih dveh metodah a z razliko, da klasificira regije vhodne slike. Rezultat evaluacije nad isto podatkovno zbirko je 86.5% top-1 točnosti. Dodatna evaluacija, ki testira količino razpoznanih objektov v slikah z več različnimi objekti hrane je dokazala da metoda razpozna 64% objektov na slikah. In this thesis we address the problems of image classification and image captioning with three implemented methods with neural networks (food classification, food captioning and food captioning by region-proposal). The methods were trained and tested on a 21-category food image dataset with 1470 images and a 2-category food caption dataset with 750 caption sentences. The first method—food classification method—uses the architecture of the GoogLeNet-Inception-v3 model trained on our food dataset, achieving a top-1 prediction accuracy of 82.4% and top-5 prediction accuracy of 98%. The second method—food captioning method—uses the Show and Tell architecture trained on our food caption dataset, achieving a perplexity score of 23.3. Our food visual model was used to classify the input images, but the overall results did not meet expectations, as the model does not correctly caption images containing multiple foods. The third method—food captioning with region proposal—uses our food classification method to classify images and performs better than the food-classification method alone, achieving a prediction accuracy of 86.5%. Additionally, this third method summarizes the contents of images containing different types of food with an accuracy score of 64%. računalniški vid računalništvo strojno učenje computer vision computer science machine learning true false false Angleški jezik Slovenski jezik Magistrsko delo/naloga 2017-12-14 08:52:02 2017-12-14 08:52:02 2022-08-16 10:24:10 0000-00-00 00:00:00 2017 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 1970-01-01 19508 Sedevcic_Kevin_-_Razpoznavanje_hrane_na_podlagi_slik_z_nevronskimi_mrezami.pdf Sedevcic_Kevin_-_Razpoznavanje_hrane_na_podlagi_slik_z_nevronskimi_mrezami.pdf 1 A0793367EA5EC8DDE637611097C569C6 7f79c7881b9d623d61b91c379c0b751c7772345ce847962f78dca5d091744039 76ba8066-a1b4-11eb-a523-00155dcfd717 20.500.12556/rul/5903a600-8980-480b-8b16-c5de026085dc https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=108276 Fakulteta za računalništvo in informatiko 0 0 0