<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Content Based Image Retrieval (CBIR) system is an efficient search engine which has the potentiality of retrieving the images from huge repositories by extracting the visual features. It includes color, texture and shape. Texture is the most eminent feature among all. This investigation focuses upon the classification complications that crop up in case of big datasets. In this, texture techniques are explored with machine learning algorithms in order to increase the retrieval efficiency. We have tested our system on three texture techniques using various classifiers which are Support vector machine, K-Nearest Neighbor (KNN), Naïve Bayes and Decision Tree (DT). Variant evaluation metrics precision, recall, false alarm rate, accuracy etc. are figured out to measure the competence of the designed CBIR system on two benchmark datasets, i.e. Wang and Brodatz. Result shows that with both these datasets the KNN and DT classifier hand over superior results as compared to others.</p>
</abstract>

Content Based Image Retrieval (CBIR) system is an efficient search engine which has the potentiality of retrieving the images from huge repositories by extracting the visual features. It includes color, texture and shape. Texture is the most eminent feature among all. This investigation focuses upon the classification complications that crop up in case of big datasets. In this, texture techniques are explored with machine learning algorithms in order to increase the retrieval efficiency. We have tested our system on three texture techniques using various classifiers which are Support vector machine, K-Nearest Neighbor (KNN), Naïve Bayes and Decision Tree (DT). Variant evaluation metrics precision, recall, false alarm rate, accuracy etc. are figured out to measure the competence of the designed CBIR system on two benchmark datasets, i.e. Wang and Brodatz. Result shows that with both these datasets the KNN and DT classifier hand over superior results as compared to others.

Employing Divergent Machine Learning Classifiers to Upgrade the Preciseness of Image Retrieval Systems

Guru Gobind Singh Indraprastha University New Delhi

Maharaja Surajmal Institute of Technology

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Content Based Image Retrieval (CBIR) system is an efficient search engine which has the potentiality of retrieving the images from huge repositories by...

Employing Divergent Machine Learning Classifiers to Upgrade the Preciseness of Image Retrieval Systems

Data publikacji: 13 wrz 2020

Zakres stron: 75 - 85

Otrzymano: 12 mar 2020

Przyjęty: 06 lip 2020

DOI: https://doi.org/10.2478/cait-2020-0029

Słowa kluczowe
Support vector machines, K-Nearest Neighbour (KNN), Decision tree, Naïve bayes, False alarm rate

© 2020 Shefali Dhingra et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Employing Divergent Machine Learning Classifiers to Upgrade the Preciseness of Image Retrieval Systems

Data publikacji: 13 wrz 2020

Zakres stron: 75 - 85

Otrzymano: 12 mar 2020

Przyjęty: 06 lip 2020

DOI: https://doi.org/10.2478/cait-2020-0029

Słowa kluczoweSupport vector machines, K-Nearest Neighbour (KNN), Decision tree, Naïve bayes, False alarm rate

© 2020 Shefali Dhingra et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Support vector machines, K-Nearest Neighbour (KNN), Decision tree, Naïve bayes, False alarm rate