Wavelet Based Image Retrieval Method

Abstract

Abstract-A novel method for retrieving image based on color and texture extraction is proposed for improving the accuracy. In this research, we develop a novel image retrieval method based on wavelet transformation to extract the local feature of an image, the local feature consist color feature and texture feature. Once an image taking into account, we transform it using wavelet transformation to four sub band frequency images. It consists of image with low frequency which most same with the source called approximation (LL), image containing high frequency called horizontal detail (LH), image containing high frequency called vertical detail (HL), and image containing horizontal and vertical detail (HH). In order to enhance the texture and strong edge, we combine the vertical and horizontal detail to be other matrix. The next step is we estimate the important point called significant point by threshold the high value. After the significant points have been extracted from image, the coordinate of significant points will be used for knowing the most important information from the image and convert into small regions. Based on these significant point coordinates, we extract the image texture and color locally. The experimental results demonstrate that our method on standard dataset are encouraging and outperform the other existing methods, improved around 11 %.

Method for Determination of Support Length of Daubechies Basis Function for Wavelet MRA based Moving Characteristic Estimation

“…Wavelet based image retrieval method is proposed [26]. Meanwhile, DP matching based image retrieval method with wavelet Multi Resolution Analysis: MRA which is robust against magnification of image size is conducted [27].…”

Content-based image retrieval based on combination of texture and colour information extracted in spatial and frequency domains

“…In Wei-Ying and Manjunath (1995), Gabor wavelet and orthogonal wavelet-based texture features for texture classification and discrimination were compared. Arai and Rahmad (2012) developed a novel CBIR system based on wavelet transformation to extract the local features of an image – the local feature consists of colour and texture information. In this respect, the first image transformed to the frequency domain using wavelet.…”

Look-a-Like: A Fast Content-Based Image Retrieval Approach Using a Hierarchically Nested Dynamically Evolving Image Clouds and Recursive Local Data Density

“… It is used as the final set of features. Any decomposition of the image into a wavelet involves a pair of waveforms; the high‐frequency components correspond to the details of an image, whereas the low‐frequency components correspond to its smooth parts . Discrete wavelet transform (DWT) of an image as a 2D signal can be derived from a one‐dimensional (1D) DWT, implementing 1D DWT to every row then implementing a 1D DWT to every column.…”

“…Discrete wavelet transform (DWT) of an image as a 2D signal can be derived from a one‐dimensional (1D) DWT, implementing 1D DWT to every row then implementing a 1D DWT to every column. Any decomposition of the 2D images into a wavelet involves four subband elements representing LL (approximation), HL (vertical detail), LH (horizontal detail), and HH (detail), respectively . The DWT of a signal x is calculated by passing it through a low pass filter with impulse response h and high pass filter g .…”