Region-Based Image Retrieval by Use of Relevance Feedback
Date Issued
2005
Date
2005
Author(s)
Hsieh, Ming-Han
DOI
en-US
Abstract
With the exponential growth of multi-media data, finding images in a large database has become more difficult. Region-based image retrieval (RBIR) is used for solving this problem in this thesis. There are some differences between RBIR and traditional content-based image retrieval (CBIR) systems. CBIR is focused on the similarity of global images and RBIR is focused on the similarity of the local image regions. We apply the watershed segmentation to segment each image into some regions. To classify these regions, the fuzzy k-means clustering algorithm is time-wasting and uses too much space to store the information about the regions. We propose a modified fuzzy k-means clustering algorithm to classify regions efficiently. In order to accelerate our system, we propose a new method for filtering which can filter out many unsuitable images. The candidate images are ranked based on their similarity measure. After our system retrieves the images, the user is able to give feedback to the system. Based on user’s feedback information, our system will retrieve the images that are even closer to the user’s intent.
Subjects
關聯回饋
影像檢索
Region-Based
Image Rterieval
Relevance Feedback
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-94-R92922053-1.pdf
Size
23.31 KB
Format
Adobe PDF
Checksum
(MD5):9255be1b32a789e3878763040b7d2479