What is content-based image retrieval system?
Content-based image retrieval (CBIR) is a framework that can overcome the abovementioned problems as it is based on the visual analysis of contents that are part of the query image. The discriminative feature representation is another main requirement for any image retrieval system [17, 18].
What are the types of image features?
Types
- Edges. Edges are points where there is a boundary (or an edge) between two image regions.
- Corners / interest points.
- Blobs / regions of interest points.
- Ridges.
- Low-level.
- Shape based.
- Flexible methods.
- Certainty or confidence.
How is content based image retrieval ( CBIR ) used?
Content-based image retrieval (CBIR) is a widespread technique gradually applied in retrieval systems [1]. In CBIR, images retrieval is done by using visual characteristics also known as features, extracted from the database. CBIR system’s retrieval accuracy and efficiency rely greatly on the adopted visual feature.
How is content based image retrieval different from pattern recognition?
The problem of content-based image retrieval is different than the conventional computer vision-based pattern recognition task. The fundamental difference between the two is that in the latter, we are looking for exact match for the object to be searched with as small and as accurate a retrieved list as possible.
How are image features used in image retrieval?
Recent image retrieval techniques are focusing on multiple image features for the efficient image retrieval. It has been an inevitable requirement to fetch the images from a variety of semantic groups and datasets.
Who is the founder of content based image retrieval?
Srirangaraj Setlur, in Handbook of Statistics, 2015 Content-based image retrieval (Datta et al., 2008; Smeulders et al., 2000) is a very mature, yet on-going, open area of research.