Machine Learning Models for Cultural Heritage Image Classification: Comparison Based on Attribute Selection

Abstract

Image classification is one of the most important tasks in the digital era. In terms of cultural heritage, it is important to develop classification methods that obtain good accuracy, but also are less computationally intensive, as image classification usually uses very large sets of data. This study aims to train and test four classification algorithms: (i) the multilayer perceptron, (ii) averaged one dependence estimators, (iii) forest by penalizing attributes, and (iv) the k-nearest neighbor rough sets and analogy based reasoning, and compares these with the results obtained from the Convolutional Neural Network (CNN). Three types of features were extracted from the images: (i) the edge histogram, (ii) the color layout, and (iii) the JPEG coefficients. The algorithms were tested before and after applying the attribute selection, and the results indicated that the best classification performance was obtained for the multilayer perceptron in both cases.

Context-Based Cultural Visits

“…Two types of convolutional neural networks were applied, AlexNet and Inception V3, as well as two residual networks, ResNet and Inception-ResNet-v2. The dataset included more than 10,000 images, and the obtained results revealed that deep learning methods achieved better accuracy when dealing with complex problems such as image classification, compared to other cutting-edge techniques [37].…”

“…The results revealed that, in terms of accuracy, RNN achieved the highest performance, classifying 92% of the text data accurately. In terms of CNN, the best accuracy was achieved for image and video classification (76% each), while audio classification obtained only 57% accuracy [37].…”

Cultural Events Classification using Hyper-parameter Optimization of Deep Learning Technique

“…Several studies have developed cultural heritage documentation and implemented different methods to classify cultural heritage [13]. For example, in the study of architectural heritage, the authors proposed the image dataset of more than 10.000 images classified into ten classes, i.e., different architectural heritage types such as columns, domes, gargoyles, and vault [14].…”

Rseslibknn Maki̇ne Öğrenmesi̇ Yöntemi̇ Kullanilarak Parki̇nson Hastaliğinin Tanisi

“…Sınıflandırma, sette en yakın komşuları bulmak ve karar için oy vermek suretiyle yapılır. RSeslibKnn, görüntü sınıflandırmasında kullanılan son teknoloji tekniklerin bir temsili, ek olarak geliştirilen kNN modelidir [14].…”