By Topic

Automatic Object Extraction Over Multiscale Edge Field for Multimedia Retrieval

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Kiranyaz, S. ; Inst. of Signal Process., Tampere Univ. of Technol. ; Ferreira, M. ; Gabbouj, M.

In this work, we focus on automatic extraction of object boundaries from Canny edge field for the purpose of content-based indexing and retrieval over image and video databases. A multiscale approach is adopted where each successive scale provides further simplification of the image by removing more details, such as texture and noise, while keeping major edges. At each stage of the simplification, edges are extracted from the image and gathered in a scale-map, over which a perceptual subsegment analysis is performed in order to extract true object boundaries. The analysis is mainly motivated by Gestalt laws and our experimental results suggest a promising performance for main objects extraction, even for images with crowded textural edges and objects with color, texture, and illumination variations. Finally, integrating the whole process as feature extraction module into MUVIS framework allows us to test the mutual performance of the proposed object extraction method and subsequent shape description in the context of multimedia indexing and retrieval. A promising retrieval performance is achieved, and especially in some particular examples, the experimental results show that the proposed method presents such a retrieval performance that cannot be achieved by using other features such as color or texture

Published in:

Image Processing, IEEE Transactions on  (Volume:15 ,  Issue: 12 )