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.-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.