Efficiency comparative analysis between two search algorithms using DT CWT for content-based image retrieval

Abstract

The following paper presents a comparative analysis on the efficiency of two search algorithms for Content-Based Image Retrieval. Both algorithms are designed using the Dual-Tree Complex Wavelet Transform for image feature extraction and Hausdorff distance for similarity distance computation. The difference in the steps of the algorithms produces difference in the final result. To estimate the efficiency of the algorithms, we performed some experiments and compared their results. They clearly show that one of the algorithms provides much higher retrieval rate and system performance than the other.

Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform

“…The directional local extrema patterns (DLEP) [10] extract directional edge information based on local extrema in 0 • , 45 • , 90 • , and 135 • directions in an image. In addition, CBIR methods derived from wavelet-based texture features from Gabor wavelets [11], discrete wavelet transforms (DWT) [12], DTCWT [13], and shape-adaptive discrete wavelet transforms (SA-DWT) [14] have been studied. SA-DWT can work on each image region separately and preserve its spatial and spectral properties.…”