There were two approaches to content-based image retrieval initially.
The first one is based on attribute representation proposed by database researchers where image contents are defined as a set of attributes which are extracted manually and are maintained within the
framework of conventional database management systems. Queries are specified using these attributes. This obviously involves
High-level of image abstraction.
The second approach which was presented by image interpretation researchers depends on an integrated feature-extraction / object-recognition subsystem to overcome the limitations of attribute-based retrieval. This system automates the feature-extraction and object recognition tasks that occur when an image is inserted into the database. These automated approaches to object recognition are computationally expensive, difficult and tend to be domain specific.
Recent content-based image retrieval research tries to combine both of these above mentioned approaches and has developed efficient image representations and data models, query-processing algorithms, intelligent query interfaces and domain-independent system architecture.
There are two major categories of features. One is basic which is concerned with extracting boundaries of the image and the other one is logical which defines the image at various levels of details.
Regardless of which approach is used, the retrieval in content-based image retrieval is done by color, texture, sketch, shape, volume, spatial constraints, browsing, objective attributes, subjective attributes, motion, text
and domain concepts
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