Authors:
Leonardo Vidal Batista
1
;
Moab Mariz Meira
1
and
Nicomedes L. Cavalcanti Júnior
2
Affiliations:
1
DI / UFPB, Brazil
;
2
Centro de Informática - CIn / UFPE, Brazil
Keyword(s):
Content-based Image Retrieval, Texture classification, Lempel-Ziv-Welch.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
This paper presents a method for content-based texture image retrieval using the Lempel-Ziv-Welch (LZW) compression algorithm. Each texture image in the database is processed by a global histogram equalization filter, and then an LZW dictionary is constructed for the filtered texture and stored in the database. The LZW dictionaries thus constructed comprise a statistical model to the texture. In the query stage, each texture sample to be searched is processed by the histogram equalization filter and successively encoded by the LZW algorithm in static mode, using the stored dictionaries. The system retrieves a ranked list of images, sorted according to the coding rate achieved with each stored dictionary. Empirical results with textures from the Brodatz album show that the method achieves retrieval accuracy close to 100%.