Beta
ManageSearchChatDiscover
128900

New Image Classification Framework for Improving Image Retrieval Based on Relevance Feedback.

Article

Last updated: 22 Jan 2023

OverviewSimilar Items

Subjects

-

Tags

Computer Engineering and Systems

Abstract

Content-based image retrieval (CBIR) is a promising technology to assist image finding, CBIR retrieves images by visual features inherent in images. Relevance feedback allows the user to reflect his preference to the system, then the system can reformulate the query according to the positive and/or negative examples responded by the user. This paper presents two efficient frameworks for image classification through the analysis of the visual features (such as color, shape, texture) of an example image. The first framework is based on comparing the norm and the direction of vectors ia multi-dimensional space for both the target and query image vectors, which allows classification according to their probabilities oli existence. The second framework depends on acquiring classification knowledge from a large empirical image database in a specific domain and utilizes that knowledge for image classification. The initial classification process is used as a training phase to feed the system with a classification tree for images in the retrieval domain. This tree is the best reduction of dimensionality that would result from all possible combinations of feature divisions. 

DOI

10.21608/bfemu.2020.128900

Keywords

Image classification, Content Based Image Retrieval, Relevance Feedback. Inference Engines, Vector Analysis, Multidimensional Vector Indexing

Authors

First Name

M.

Last Name

ElAlami

MiddleName

-

Affiliation

Department of Computer Science ., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt

Email

moh_almi@mans.eun.eg

City

Mansoura

Orcid

-

Volume

32

Article Issue

4

Related Issue

19078

Issue Date

2007-12-01

Receive Date

2007-10-11

Publish Date

2020-12-10

Page Start

10

Page End

19

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 32_Issue 4_Pages 10-19.pdf

PDF . 421.0kB

Link

https://bfemu.journals.ekb.eg/article_128900.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=128900

Order

7

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

-

Details

Type

Article

Created At

22 Jan 2023