Deakin University
Browse

Content analysis from user's relevance feedback for content-based image retrieval

Version 2 2024-06-05, 03:30
Version 1 2019-07-10, 15:23
chapter
posted on 2024-06-05, 03:30 authored by CH Wei, Chang-Tsun LiChang-Tsun Li
An image is a symbolic representation; people interpret an image and associate semantics with it based on their subjective perceptions, which involves the user’s knowledge, cultural background, personal feelings and so on. Content-based image retrieval (CBIR) systems must be able to interact with users and discover the current user’s information needs. An interactive search paradigm that has been developed for image retrieval is machine learning with a user-in-the-loop, guided by relevance feedback, which refers to the notion of relevance of the individual image based on the current user’s subjective judgment. Relevance feedback serves as an information carrier to convey the user’s information needs / preferences to the retrieval system. This chapter not only provides the fundamentals of CBIR systems and relevance feedback for understanding and incorporating relevance feedback into CBIR systems, but also discusses several approaches to analyzing and learning relevance feedback.

History

Pagination

216-234

ISBN-13

9781605661742

Publication classification

BN.1 Other book chapter, or book chapter not attributed to Deakin

Publisher

IGI Global

Title of book

Artificial Intelligence for Maximizing Content Based Image Retrieval

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC