Monuments Recognition using Deep Learning VS Machine Learning

Hesham, Shahd; Khaled, Rawan; Yasser, Dalia; Refaat, Samira; Shorim, Nada; Ismail, Fatma Helmy

doi:10.1109/ccwc51732.2021.9376029

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…Hesham et al [71] assess the ResNet and VGGNet performance for the mission of Indian archeological monument identification. Their main aim is to develop a system for automatic identification of archeological monument system in Egypt.…”

Section: Machine Learning Feature Extraction and Classification Of Archaeological Monumentmentioning

confidence: 99%

Image Abstraction Framework as a Pre-processing Technique for Accurate Classification of Archaeological Monuments Using Machine Learning Approaches

Kumar¹,

Poornima

Nagendraswamy

et al. 2021

SN COMPUT. SCI.

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This work extricates the image characteristic features for the classification of archeological monument images. At the preprocessing stage, archeological dataset sample images are treated by using structure safeguarding image abstraction framework, which can deliver the most effective image abstraction output by manipulating the perceptible features in the given low-illuminated and underexposed color image samples. Proposed abstraction-framework effectively boosted the significant image property features like color, edge, sharpness, contrast and suppresses complexity and noise. The image properties were also refined at each phase based on the attained statistical feature disposal information. The work adopted the Harris feature identification technique to identify the most significant image features in the input and enhanced images. The framework also preserves significant features in the foreground of an image by intelligently integrating the series of filters during rigorous experimental work and also diminishes the background content of an input image. The proposed archeological system evaluates every stage of the result with assorted subjective matters and calculates the image quality and properties assessment statistical attributes. By this way prominent features in an image have been recognized. The efficiency of this work has been corroborated by performing the trials on the selected archeological dataset. In addition, user's visual feedback and the standard image quality assessment techniques were also used to evaluate the proposed pre-processing framework. Based on the obtained abstraction images from the framework, this work extracts the image gray color texture features using GLCM, color texture from CTMs and deep-learning features from AlexNet for the classification of archeological monument classification. This work adopted a support vector machine as a classifier. To corroborate the efficiency of the proposed method, an experiment was conducted on our own data set of Chalukya, Kadamba, Hoysala and new engraving monuments, each domain consisting of 500 archeological data set samples with large intra-class variation, with different environmental lighting condition, low-illumination and different pose. Implementation of this work was carried out in MATLAB-2020 with HPC Nvidia Tesla P100 GPU, and obtained results show that combination of multiple features significantly improves the performance to the extent of 98.10%.

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Section: Machine Learning Feature Extraction and Classification Of Archaeological Monumentmentioning

confidence: 99%

Image Abstraction Framework as a Pre-processing Technique for Accurate Classification of Archaeological Monuments Using Machine Learning Approaches

Kumar¹,

Poornima

Nagendraswamy

et al. 2021

SN COMPUT. SCI.

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“…Public availability. The dataset aims to help the research community solve the scarcity of Egyptian ancient monument datasets that face researchers in this domain [28]. Our dataset was explicitly built to account for these difficulties.…”

Section: A Purposesmentioning

confidence: 99%

Egypt Monuments Dataset version 1: A Scalable Benchmark for Image Classification and Monument Recognition

Hassan¹,

Hamdy²,

Nasr³

2023

IJACSA

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The success of machine learning (ML) as well as deep learning (DL) depends largely on data availability and quality. The system's performance is frequently more affected by the amount and quality of its training data than by its architecture and training specifics. Consequently, demand exists for challenging datasets that both precisely measure performance and present unique challenges with real-world applications. The Egypt Monuments Dataset v1 (EGYPT-v1) is introduced as a new scalable benchmark for fine-image classification (IC) and object recognition (OR) in the domain of ancient Egyptian monuments. EGYPT-v1 dataset is by far the world's first large specified such dataset to date, with over seven thousand images and 40 distinct instance labels. The dataset composes different categories of monuments such as pyramids, temples, mummies, statues, head statues, bust statues, heritage sites, palaces and shrines. Several advanced deep network architectures were tested to appraise the classification difficulty in the EGYPT-v1 dataset, namely ResNet50, Inception V3, and LeNet5 models. The models achieved accuracy rates as follows: 99.13%, 90.90%, and 92.64%, respectively. The dataset was predominantly created by manually collecting images from the popular global online video-sharing and social media platform, Youtube, as well as WATCHiT, Egypt's top streaming entertainment service. Additionally, Wikimedia Commons, the largest crowdsourced media repository in the world, was used as a secondary source of images. The images that comprise the dataset can be accessed on the GitHub repository https://github.com/mennatallahhassan/egyptmonuments-dataset.

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“…In recognition of the monuments, the accuracy of InceptionV3, MobileNet, ResNet50, AlexNet, and VGG16 was 97.79%, 93.73%, 86.47%, 68.88%, and 61.33%, respectively. S. Hesham et al [30] applied three algorithms to compare ML and DL in the context of monument recognition in 2021 on an Indian dataset from Kaggle that includes 1,286 images. These algorithms were ResNet50, VGG16, and KNN.…”

Section: Literature Surveymentioning

confidence: 99%

دراسة استطلاعية: التعرف على الآثار باستخدام الذكاء الاصطناعي

حسن,

حمدى,

نصر

2023

النشرة المعلوماتية في الحاسبات والمعلومات

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Monuments are man-made structures that are designed to imbue a location with important meanings. They serve both a political and artistic function. There is an overwhelming amount of information and features to look into monuments. Monument work is a unique industry. It would help the economies and growth of many countries. Every culture generally has its own set of distinct characteristics, like monuments, writing, and music. Considering the history and stories associated with monuments, it is difficult to evaluate the techniques used to detect these monuments. Within the domain of object recognition and classification, monument recognition is an arduous task. Numerous difficulties must be overcome, as multiple variables can alter the recognition method. This paper has been written to study the recent research on this point from 2017 to 2023. In this study, monuments from a variety of civilizations and countries, including Egypt, India, Turkey, Paris, Greece, and Singapore, have been compared to the algorithms or methodology utilized.

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Monuments Recognition using Deep Learning VS Machine Learning

Cited by 11 publications

References 17 publications

Image Abstraction Framework as a Pre-processing Technique for Accurate Classification of Archaeological Monuments Using Machine Learning Approaches

Image Abstraction Framework as a Pre-processing Technique for Accurate Classification of Archaeological Monuments Using Machine Learning Approaches

Egypt Monuments Dataset version 1: A Scalable Benchmark for Image Classification and Monument Recognition

دراسة استطلاعية: التعرف على الآثار باستخدام الذكاء الاصطناعي

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