Classification of archaeological monuments for different art forms with an application to CBIR

Desai, Padmashree; Pujari, Jagadeesh; Ayachit, N. H.; Prasad, V. Kamakshi

doi:10.1109/icacci.2013.6637332

Cited by 19 publications

(9 citation statements)

References 10 publications

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“…The precision obtained using pre-trained CNN with the Caltech256 database is 90% for 1000 images. Padmashree Desai et al in [8][9][10][11][12][13] discusses different methods of feature extraction using wavelets, edge operators, morphological operators ad moment invariants. Performance analysis is done using different distance measures.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Approach for Content-Based Image Retrieval using VGG16 Layered Architecture and SVM: An Application of Deep Learning

Desai

Pujari²,

Sujatha

et al. 2021

SN COMPUT. SCI.

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Advancements in the sector of computer and multimedia technology and introduction of the World Wide Web have increased the volume of image databases and collections, for example medical imageries, digital libraries, art galleries which in total contain millions of images. The retrieval process of images from such huge database by traditional methods such as Text Based Image Retrieval, Color Histogram and Chi Square Distance may take a lot of time to get the desired images. It is necessity to develop an effective image retrieval system which can handle these huge amounts of images at once. The main purpose is to build a robust system that builds, executes and responds to data in an efficient manner. A Content-Based Image Retrieval (CBIR) system has been developed as an efficient image retrieval tool where user can provide their query to the system to allow it to retrieve user's desired image from the image collection. Moreover, the emergence of web development and transmission networks and also the number of images which are available to users continue to grow. We propose an effective deep learning framework based on Convolution Neural Networks (CNN) and Support Vector Machine (SVM) for fast image retrieval. Proposed architecture extracts features using CNN and classification using SVM. The results demonstrate the robustness of the system.

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Section: Literature Surveymentioning

confidence: 99%

Hybrid Approach for Content-Based Image Retrieval using VGG16 Layered Architecture and SVM: An Application of Deep Learning

Desai

Pujari²,

Sujatha

et al. 2021

SN COMPUT. SCI.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Desai et al in [11][12][13][14][15] discusses different methods of feature extraction using wavelets, edge operators, morphological operators ad moment invariants. Performance analysis is done using different distance measures.…”

Section: Related Workmentioning

confidence: 99%

Impact of Multi-Feature Extraction on Image Retrieval and classification Using Machine Learning Technique

Desai

Pujari²,

Akhila

et al. 2021

SN COMPUT. SCI.

Self Cite

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Vast information generated due to the web requires proper mechanisms and tools for efficient management and retrieval of images. This led to the development of an efficient image retrieval system. Image retrieval is the process of retrieving relevant images from a large database. Text-Based Image Retrieval (TBIR) and Content-Based Image Retrieval (CBIR) are the two efficient techniques for image retrieval. It has become an active area of research for image retrieval. Most of the systems are designed with three basic phases in CBIR, i.e. feature extraction, feature selection and similarity matching. Feature extraction can be done based on the colour, shape or texture of the image. The studies conducted so far involve CBIR systems which utilize individual features or only two or more features combined for feature extraction by applying different feature extraction and CBIR techniques. The proposed system illustrates a comparison between the performance of the existing CBIR algorithm with multi-feature extraction using discrete wavelet transform, edge histogram descriptor, sobel operator, moment invariant, histogram-oriented gradient, local binary pattern and classification using Support Vector Machine (SVM) based on the accuracy measures-precision and recall. The experimental results show that multi-feature extraction techniques give better results in some of the individual categories but retrieval results using machine learning is better with respect to average accuracy.

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“…Desai et al [ 39 ] presented a classification method of archaeological monuments using Content-based Image Retrieval (CBIR) techniques. They applied visual features and the texture of 3D shapes to learn the art form and retrieved similar images from the reference collection.…”

Section: Related Workmentioning

confidence: 99%

Approach for 3D Cultural Relic Classification Based on a Low-Dimensional Descriptor and Unsupervised Learning

Gao

Geng

Sheng

2020

Entropy

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Computer-aided classification serves as the basis of virtual cultural relic management and display. The majority of the existing cultural relic classification methods require labelling of the samples of the dataset; however, in practical applications, there is often a lack of category labels of samples or an uneven distribution of samples of different categories. To solve this problem, we propose a 3D cultural relic classification method based on a low dimensional descriptor and unsupervised learning. First, the scale-invariant heat kernel signature (Si-HKS) was computed. The heat kernel signature denotes the heat flow of any two vertices across a 3D shape and the heat diffusion propagation is governed by the heat equation. Secondly, the Bag-of-Words (BoW) mechanism was utilized to transform the Si-HKS descriptor into a low-dimensional feature tensor, named a SiHKS-BoW descriptor that is related to entropy. Finally, we applied an unsupervised learning algorithm, called MKDSIF-FCM, to conduct the classification task. A dataset consisting of 3D models from 41 Tang tri-color Hu terracotta Eures was utilized to validate the effectiveness of the proposed method. A series of experiments demonstrated that the SiHKS-BoW descriptor along with the MKDSIF-FCM algorithm showed the best classification accuracy, up to 99.41%, which is a solution for an actual case with the absence of category labels and an uneven distribution of different categories of data. The present work promotes the application of virtual reality in digital projects and enriches the content of digital archaeology.

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Classification of archaeological monuments for different art forms with an application to CBIR

Cited by 19 publications

References 10 publications

Hybrid Approach for Content-Based Image Retrieval using VGG16 Layered Architecture and SVM: An Application of Deep Learning

Hybrid Approach for Content-Based Image Retrieval using VGG16 Layered Architecture and SVM: An Application of Deep Learning

Impact of Multi-Feature Extraction on Image Retrieval and classification Using Machine Learning Technique

Approach for 3D Cultural Relic Classification Based on a Low-Dimensional Descriptor and Unsupervised Learning

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