Content Based Image Retrieval (CBIR) is an automatic process to search relevant images based on user input. The input could be parameters, sketches or example images. A typical CBIR process first extracts the image features and store them efficiently. Then it compares with images from the database and returns the results. Besides reading existing information, which already exists in digital photographs, and transformation of this information to MPEG-7, RF supports the creation of new metadata. Semantic information about the image is presented as directed graph, where the nodes reflect semantic objects, locations, agents, states, times or concepts and the edges define the relations between these semantic entities. To enhance retrieval efficiency content-based metadata is extracted and new instances of the image for faster visualization, like thumbnails, are created. The MPEG-7 description consists of the following parts: metadata description, creation information, media information, textual annotation, semantics, and visual descriptors. Index Term: Text classification; Feature selection; K-Nearest Neighbor; Naïve Bayesian
[1]
Kien A. Hua,et al.
Fast Query Point Movement Techniques for Large CBIR Systems
,
2009,
IEEE Transactions on Knowledge and Data Engineering.
[2]
Sanjay N. Talbar,et al.
Color Spaces for Transform-based Image Retrieval
,
2010
.
[3]
Deok-Hwan Kim,et al.
QCluster: relevance feedback using adaptive clustering for content-based image retrieval
,
2003,
SIGMOD '03.
[4]
Tao Qin,et al.
An active feedback framework for image retrieval
,
2008,
Pattern Recognit. Lett..
[5]
Rynson W. H. Lau,et al.
Knowledge and Data Engineering for e-Learning Special Issue of IEEE Transactions on Knowledge and Data Engineering
,
2008
.