Shape measures for content based image retrieval: a comparison
Information Processing and Management: an International Journal
A scheme of colour image retrieval from databases
Pattern Recognition Letters
Digital Image Processing
Computer and Robot Vision
Computer Vision
Texture classification using wavelet transform
Pattern Recognition Letters
Texture segmentation using wavelet transform
Pattern Recognition Letters
Image retrieval system based on color-complexity and color-spatial features
Journal of Systems and Software
Additive texture information extraction using color coherence vector
MUSP'07 Proceedings of the 7th WSEAS International Conference on Multimedia Systems & Signal Processing
ICS'06 Proceedings of the 10th WSEAS international conference on Systems
Color and texture image retrieval using chromaticity histograms and wavelet frames
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
Image retrieval using BDIP and BVLC moments
IEEE Transactions on Circuits and Systems for Video Technology
A graphical method of detecting pneumonia using chest radiograph
WSEAS Transactions on Information Science and Applications
On-line content-based image retrieval system using joint querying and relevance feedback scheme
WSEAS Transactions on Computers
RSTC-invariant object representation with 2D modified Mellin-Fourier transform
WSEAS Transactions on Signal Processing
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Texture gradient is a popular operation for extracting features used for content-based image retrieval (CBIR) of texture images. It is useful for depicting gradient magnitude and direction of adjacent pixels in an image. In this thesis, we proposed two methods for retrieving texture images. In the first method, discrete wavelet transform (DWT) and gradient operation were combined to extract features of an image with principal component analysis (PCA) used to determine weights of individual extracted features, while in the second method, only gradient operation without involve ent of discrete wavelet transform was used to extract features. The Brodatz Album which contains 112 texture images, each has the size of 512×512 pixels, was used to evaluate the performance of the proposed methods. Before experiment, each image was cut into sixteen 128×128 non-overlapping sub-images, thus creating a database consisting of 1792 images. Regarding the number of features, a total of 126 features were extracted in the first method by calculating gradients after discrete wavelet transforms of the texture image, while in the second method only 54 features were extracted from each gradient image. By integrating useful features, image retrieval systems for retrieving texture images have been designed. The results show that the two proposed methods have been demonstrated to be able to achieve better retrieval accuracy than the method proposed by Huang and Dai. Additionally, our proposed systems, especially the second proposed method, use fewer features which significantly decrease the retrieval time compared to the previous investigation.