Description: Nowadays, Content-Based Image Retrieval (CBIR) is the
mainstay of image retrieval systems. To understand the query
semantics and users expectations so as to communicate faithful
results in terms of accuracy, Relevance Feedback (RF) was
incorporated to CBIR systems. By allowing the user to assess
iteratively the answers as relevant/irrelevant or even giving
him/her the opportunity to specify a degree of relevance (user’s
feedbacks) , the system creates a new query that better captures
the user s needs, hence raising the opportunity to get more
relevant image results.
In this paper, we have focused on CBIR and basic concepts
pertaining to it, as well as Relevance Feedback and its various
mechanisms. An important contribution in this work is a
comparative analysis of CBIR systems using reference feedback:
major models and approaches are discussed in detail from early
heuristic methods to recently optimal learning algorithms, with
more emphasize on their advantages and weaknesses.
To Search:
File list (Check if you may need any files):
article.pdf
Download