An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval

Abstract

Content-based image retrieval (CBIR) is a methodology used to search indistinguishable images across any vast repository. Texture, Color and Shape are among the most prominent features of any CBIR system. Two texture descriptors namely Gray level Co-occurence matrix (GLCM) and Discrete wavelet transform (DWT) have been utilized here for the formation of a hybrid texture descriptor, denoted as (Co-DGLCM). To enhance the retrieval accuracy of the proposed system, a framework of an Extreme learning machine (ELM) with Relevance feedback (RF) has also been used. This technique provides simultaneously spatial relationship and information related to frequency in co-occuring local patterns of an image. Two benchmark texture databases namely Brodatz and MIT-Vistex have been tested and results are obtained in terms of accuracy, total average recall and total average precision which is 96.35% and 97.34% respectively on the two databases.

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

“…In CBIR systems, the capability of a particular system can be concluded with respect to many evaluation parameters [39]- [40]. Precision and Recall are the most well-known evaluation metrics.…”

An Employment of Neural Network Classifiers to evaluate the Performance of Color Feature Descriptors in an Image Retrieval System: An Experimental Survey

“…Texture is another feature of an image, which defines the inborn appearance patterns of an image. A large quantity of techniques like Discrete Wavelet Transform (DWT), Curvelet Transform [6], Gabor Transform, Gray level Co-occurance Matrix (GLCM) [7], Color Cooccurance Matrix (CCM), Tamura features etc. are being http://dx.doi.org/10.12785/ijcds/090412 http://journal.uob.edu.bh used for texture extraction.…”