We propose an innovative and efficient approach to improve K-view-template (K-view-T) and K-view-datagram (K-view-D) algorithms for image texture classification. The proposed approach, called the weighted K-view-voting algorithm (K-view-V), uses a novel voting method for texture classification and an accelerating method based on the efficient summed square image (SSI) scheme as well as fast Fourier transform (FFT) to enable overall faster processing. Decision making, which assigns a pixel to a texture class, occurs by using our weighted voting method among the "promising" members in the neighborhood of a classified pixel. In other words, this neighborhood consists of all the views, and each view has a classified pixel in its territory. Experimental results on benchmark images, which are randomly taken from Brodatz Gallery and natural and medical images, show that this new classification algorithm gives higher classification accuracy than existing K-view algorithms. In particular, it improves the accurate classification of pixels near the texture boundary. In addition, the proposed acceleration method improves the processing speed of K-view-V as it requires much less computation time than other K-view algorithms. Compared with the results of earlier developed K-view algorithms and the gray level co-occurrence matrix (GLCM), the proposed algorithm is more robust, faster, and more accurate.