Rotation-invariant and scale-invariant Gabor features for texture image retrieval
Image and Vision Computing
A hybrid genetic algorithm and particle swarm optimization for multimodal functions
Applied Soft Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Breast mass classification based on cytological patterns using RBFNN and SVM
Expert Systems with Applications: An International Journal
Journal of Visual Communication and Image Representation
Rotation Invariant Curvelet Features for Region Based Image Retrieval
International Journal of Computer Vision
Rotation-Invariant Texture Image Retrieval Using Rotated Complex Wavelet Filters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An SVD-based image watermarking in wavelet domain using SVR and PSO
Applied Soft Computing
Discriminative features for texture description
Pattern Recognition
Rotation-invariant texture retrieval with gaussianized steerable pyramids
IEEE Transactions on Image Processing
Journal of Systems and Software
Particle Swarm Optimization for the Vehicle Routing Problem with Stochastic Demands
Applied Soft Computing
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This paper presents a novel rotation-invariant texture image retrieval using particle swarm optimization (PSO) and support vector regression (SVR), which is called the RTIRPS method. It respectively employs log-polar mapping (LPM) combined with fast Fourier transformation (FFT), Gabor filter, and Zernike moment to extract three kinds of rotation-invariant features from gray-level images. Subsequently, the PSO algorithm is utilized to optimize the RTIRPS method. Experimental results demonstrate that the RTIRPS method can achieve satisfying results and outperform the existing well-known rotation-invariant image retrieval methods under considerations here. Also, in order to reduce calculation complexity for image feature matching, the RTIRPS method employs the SVR to construct an efficient scheme for the image retrieval.