Content Based Image and Video Retrieval: A Compressive Review
Sheetal Deepak Patil
Sheetal Deepak Patil*, ME, Government College of Engineering, Aurangabad (Maharashtra), India.
Manuscript received on June 06, 2021. | Revised Manuscript received on June 15, 2021. | Manuscript published on June 30, 2021. | PP: 243-247 | Volume-10 Issue-5, June 2021. | Retrieval Number: 100.1/ijeat.E27830610521 | DOI: 10.35940/ijeat.E2783.0610521
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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: Content-based image retrieval is quickly becoming the most common method of searching vast databases for images, giving researchers a lot of room to develop new techniques and systems. Likewise, another common application in the field of computer vision is content-based visual information retrieval. For image and video retrieval, text-based search and Web-based image reranking have been the most common methods. Though Content Based Video Systems have improved in accuracy over time, they still fall short in interactive search. The use of these approaches has exposed shortcomings such as noisy data and inaccuracy, which often result in the showing of irrelevant images or videos. The authors of the proposed study integrate image and visual data to improve the precision of the retrieved results for both photographs and videos. In response to a user’s query, this study investigates alternative ways for fetching high-quality photos and related videos.
Keywords: Image and Video Retrieval, Content-Based, Similar Match Etc.
Scope of the Article: Information Retrieval