Global Windowing Based Thepade's Sorted N-Ary Block Truncation Coding (TSnBTC) for Content Based Video Retrieval with Various Similarity Measures

Soniminde, Nikhil V.; Thenade, Sudeen D.

doi:10.1109/iccubea.2018.8697852

Cited by 2 publications

(1 citation statement)

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“…In image retrieval using TSBTC [15][16][17], the pixel values of an image are sorted in the ascending order, and the median value is calculated. en, the lower mean is calculated using the pixel values below the median, and the upper mean is computed using pixel values above the median.…”

Section: Color Features Used In Image Retrievalmentioning

confidence: 99%

Image Retrieval Using Low Level and Local Features Contents: A Comprehensive Review

Dewan¹,

Thepade²

2020

Applied Computational Intelligence and Soft Computing

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Billions of multimedia data files are getting created and shared on the web, mainly social media websites. The explosive increase in multimedia data, especially images and videos, has created an issue of searching and retrieving the relevant data from the archive collection. In the last few decades, the complexity of the image data has increased exponentially. Text-based image retrieval techniques do not meet the needs of the users due to the difference between image contents and text annotations associated with an image. Various methods have been proposed in recent years to tackle the problem of the semantic gap and retrieve images similar to the query specified by the user. Image retrieval based on image contents has attracted many researchers as it uses the visual content of the image such as color, texture, and shape feature. The low-level image features represent the image contents as feature vectors. The query image feature vector is compared with the dataset images feature vectors to retrieve similar images. The main aim of this article is to appraise the various image retrieval methods based on feature extraction, description, and matching content that has been presented in the last 10–15 years based on low-level feature contents and local features and proposes a promising future research direction for researchers.

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Section: Color Features Used In Image Retrievalmentioning

confidence: 99%