Comparison of Fundamental Learning Strategies for CBIR using Deep Learning Methods

(E-pub Abstract Ahead of Print)

Author(s): Meenakshi Garg*, Manisha Malhotra, Harpal Singh

Journal Name: Recent Advances in Electrical & Electronic Engineering
Formerly Recent Patents on Electrical & Electronic Engineering


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Abstract:

Background: Photo retrieval based on contents is primarily used to retrieve photographs from a broad database. CBIR, also named "search by image," is an al-lowing technology that handles computerized images by its recognizable attributes.

Methods: In other words, CBIR is a method for recovery of images that does not rely on annotations or keywords but on the characteristics of the images directly taken from the pictures. CBIR systems rely on the use of machine display methods in broad datasets for the image retrieval issue. The CBIR technology is the retrieval from a cluster of photos or archive of the most visually similar photographs to a particular query file.It is really useful for scanning photos, medical research etc. in other fields such as photography. It may be hard to visually find the images by inserting the metadata or keywords into a large database and cannot catch the keyword for identifying this image. CBIR allows the extraction of similar photographs from a digital archive with no labeling of photographs. The Deep Neural Network and Neuro-Fuzzy classification are contrasted in this article. They both have numerous findings and numerous tests to forecast the picture.

Results: The analysis of the neuro-fuzzy and deep neural network methods we suggest reveals that the precision is increased.

Conclusion: Accuracy values for DNN and Neuro-Fuzzy Classifier process are74.6% and 75.4%. For the validity of the proposed process, the visual and qualitative findings are provided.

Keywords: Machine learning, deep neural networks, neuro-fuzzy classifier, content based image retrieval, text-based image recovery, bag of visual words.

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Article Details

(E-pub Abstract Ahead of Print)
DOI: 10.2174/2352096514999210128195839
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