Content-based image retrieval using the combination of the fast wavelet transformation and the colour histogram

Abstract

In this study, an attempt has been made to study an image retrieval technique based on the combination of Haar wavelet transformation using lifting scheme and the colour histogram (CH) called lifting wavelet-based colour histogram. The colour feature is described by the CH, which is translation and rotation invariant. The Haar wavelet transformation is used to extract the texture features and the local characteristics of an image, to increase the accuracy of the retrieval system. The lifting scheme reduces the processing time to retrieve images. The experimental results indicate that the proposed technique outperforms the other schemes, in terms of the average precision, the average recall and the total average precision/recall.

Class wise image retrieval through scalable color descriptor and edge histogram descriptor

“…Singha et al (2012) designed a signature by combining color and texture information to enhance the accuracy of CBIR, to extract color features they used color histogram while fast wavelet transform was utilized to extract texture.…”

Content-based texture image retrieval by histogram of curvelets

“…Precision is the fraction of retrieved instances that are relevant, while recall is the fraction of relevant instances that are retrieved [19]. They are defined by Eqs.…”

Comparative study on Content based Image Retrieval based on Gabor Texture Features at Different Scales of Frequency and Orientations

“…In recent years, many scholars began to study high-level semantic features of images. In practice, a feature or a combination of several features is often used to search for images [21] and in this study the feature vectors based on Gabor texture are used to search for images.…”