Content-Based Image Retrieval Using FAST Machine Learning Approach in Cloud Computing

Sharmi, N.; Shameem, P. Mohamed; Parvathy, R

doi:10.1007/978-981-13-1936-5_46

Cited by 2 publications

(1 citation statement)

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“…FAST properties corner over edge detection because a corner has an intensity variation in two dimensions which makes it a point of interest. Sharmi et al (Sharmi et al, 2018) proposed a CBIR method in cloud computing based on FAST to represent features. These features are encrypted by stream chipper to preserve images' privacy before storing them on the cloud.…”

Section: Features From Accelerated Segment Test (Fast)mentioning

confidence: 99%

Content-based image retrieval: A review of recent trends

2021

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With the availability of internet technology and the low-cost of digital image sensor, enormous amount of image databases have been created in different kind of applications. These image databases increase the demand to develop efficient image retrieval search methods that meet user requirements. Great attention and efforts have been devoted to improve content-based image retrieval method with a particular focus on reducing the semantic gap between low-level features and human visual perceptions. Due to the increasing research in this field, this paper surveys, analyses and compares the current state-of-the-art methodologies over the last six years in the CBIR field. This paper also provides an overview of CBIR framework, recent low-level feature extraction methods, machine learning algorithms, similarity measures, and a performance evaluation to inspire further research efforts.

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Section: Features From Accelerated Segment Test (Fast)mentioning

confidence: 99%