Performance Comparison Between SURF and SIFT for Content-Based Image Retrieval

Malik, Najeeb ur Rehman; Airij, Awais Gul; Memon, Sheeraz; Panhwar, Yasmeen Naz; Abu-Bakar, S. A. R.; El-Khoreby, Mohamed A.

doi:10.1109/icsipa45851.2019.8977732

Cited by 6 publications

(1 citation statement)

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“…The reason SIFT gives better and more reliable matching than those obtained from its predecessor descriptors lies in the cascade filtering approach that is used to detect the features which in return transforms the image data into scale-invariant coordinates relative to the local features. Lowe formulated SIFT algorithm into four major computational stages: a)Scale-Space Extrema Detection b)Keypoint Localization c)Orientation Assignment d)Keypoint Descriptor [55].…”

Section: Scale Invariant Feature Transform (Sift)mentioning

confidence: 99%

An Overview of Content-Based Image Retrieval Methods and Techniques

Hadid,

Hussein,

Al-Qaysi

et al. 2023

ijcsm

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With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively and efficiently from a large-scale database with an input image. In the past ten years, great efforts have been made for new theories and models of CBIR, and many effective CBIR algorithms have been established. Content-based image retrieval helps to discover identical images in a big dataset that match a query image. The query image's representative feature similarities to the dataset images typically assist in ranking the images for retrieval. There are various past studies on different handicraft feature descriptors according to the visual features that describe the images: color, texture, and shape. However, deep learning has been the dominant alternative to manually planned feature engineering; it automatically takes the features from the data. The current work reviews recent advancements in content-based image retrieval. For a deeper understanding of the advancement, the explanation of current state-of-the-art approaches from various vantage points is also conducted. This review employs a taxonomy encompassing various retrieval networks, classification types, and descriptors and this study will help researchers make more progress in image retrieval

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Section: Scale Invariant Feature Transform (Sift)mentioning

confidence: 99%