Assorted Techniques for Defining Image Descriptors to Augment Content Based Classification Accuracy
Rik Das1, Mohammad Arshad2, Pankaj Kumar Manjhi3
1Rik Das*, PGPM – Information Technology, Xavier Institute of Social Service, Ranchi, India.
2Mohammad Arshad, MCA Department, Vinoba Bhave University, Hazaribag, India.
3Pankaj Kumar Manjhi, University Department of Mathematics, Vinoba Bhave University, Hazaribag, India.
Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 105-108 | Volume-9 Issue-3, February, 2020. | Retrieval Number: B4208129219/2020©BEIESP | DOI: 10.35940/ijeat.B4208.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Image data has turned out to be a significant means of expression with the advancements of digital image processing technologies. Image capturing devices has now transformed to commodities due to smart integration with cell phones and other useful devices. Huge amount of images are getting accumulated daily in gigantic databases which requires categorization for prompt retrieval in real time. Content based image classification (CBIC) thus gained it’s popularity in classifying images to their corresponding categories. Feature extraction techniques are the foundation of CBIC to represent the image data in the form of feature vectors. This work has implemented three different feature extraction techniques from spatial domain, transform domain and deep learning domain. The three different feature vectors feature vector are contrasted to investigate the robustness of descriptor definition for content based image classification.
Keywords: Binarization, image transform, pretrained CNN, feature extraction, image classification