Content-based image retrieval using color and texture fused features

Authors:
Jun Yue;Zhenbo Li;Lu Liu;Zetian Fu
Affiliations:
School of Information Science and Engineering, LUDONG University, YanTai, 264025, China and College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China;College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China;College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China;College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2011

Citing 10
Cited 3

A unified framework for semantics and feature based relevance feedback in image retrieval systems

MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years

IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries

IEEE Transactions on Pattern Analysis and Machine Intelligence
Supporting Content-based Queries over Images in MARS

ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
Image retrieval system based on color-complexity and color-spatial features

Journal of Systems and Software
Content Based Image Retrieval Using Color, Texture and Shape Features

ADCOM '07 Proceedings of the 15th International Conference on Advanced Computing and Communications
Image retrieval: Ideas, influences, and trends of the new age

ACM Computing Surveys (CSUR)
An interactive approach for CBIR using a network of radial basis functions

IEEE Transactions on Multimedia
Relevance feedback: a power tool for interactive content-based image retrieval

IEEE Transactions on Circuits and Systems for Video Technology
Color and texture descriptors

IEEE Transactions on Circuits and Systems for Video Technology

Content-based image retrieval approach for biometric security using colour, texture and shape features controlled by fuzzy heuristics

Journal of Computer and System Sciences
Texture feature extraction based on fractional mask convolution with cesáro means for content-based image retrieval

PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
A new matching strategy for content based image retrieval system

Applied Soft Computing

Quantified Score

Hi-index	0.98

Visualization

Abstract

This paper presents a method to extract color and texture features of an image quickly for content-based image retrieval (CBIR). First, HSV color space is quantified rationally. Color histogram and texture features based on a co-occurrence matrix are extracted to form feature vectors. Then the characteristics of the global color histogram, local color histogram and texture features are compared and analyzed for CBIR. Based on these works, a CBIR system is designed using color and texture fused features by constructing weights of feature vectors. The relevant retrieval experiments show that the fused features retrieval brings better visual feeling than the single feature retrieval, which means better retrieval results.