The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Mosaic-Based Relevance Feedback for Image Retrieval
|
|
Author(s): Odej Kao (University of Paderborn, Germany)and Ingo la Tendresse (Technical University of Clausthal, Germany)
Copyright: 2005
Pages: 5
Source title:
Encyclopedia of Data Warehousing and Mining
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59140-557-3.ch159
Purchase
|
Abstract
A standard approach for content-based image retrieval (CBIR) is based on the extraction and comparison of features usually related to dominant colours, shapes, textures and layout (Del Bimbo, 1999). These features are a-priori defined and extracted, when the image is inserted into the database. At query time the user submits a similar sample image (query-by-sample-image) or draws a sketch (query-by-sketch) of the sought archived image. The similarity degree of the current query image and the target images is determined by calculation of a multidimensional distance between the corresponding features. The computed similarity values allow the creation of an image ranking, where the first k, usually k=32 or k=64, images are considered retrieval hits. These are chained in a list called ranking and then presented to the user. Each of these images can be used as a starting point for a refined search in order to improve the obtained results.
Related Content
|
Md Sakir Ahmed, Abhijit Bora.
© 2024.
15 pages.
|
|
Lakshmi Haritha Medida, Kumar.
© 2024.
18 pages.
|
|
Gypsy Nandi, Yadika Prasad.
© 2024.
16 pages.
|
|
Saurav Bhattacharjee, Sabiha Raiyesha.
© 2024.
14 pages.
|
|
Naren Kathirvel, Kathirvel Ayyaswamy, B. Santhoshi.
© 2024.
26 pages.
|
|
K. Sudha, C. Balakrishnan, T. P. Anish, T. Nithya, B. Yamini, R. Siva Subramanian, M. Nalini.
© 2024.
25 pages.
|
|
Sabiha Raiyesha, Papul Changmai.
© 2024.
28 pages.
|
|
|