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A fast and efficient approach to color image segmentation and texture feature extraction is developed. In the proposed image segmentation algorithm, a new quantization technique for HSV color space is implemented to generate a color histogram and a gray histogram for K-Means clustering, which operates across different dimensions in HSV color space. Then a texture feature extraction method for content-based image retrieval, Label Wavelet Transform (LWT), is established based on the segmentation result. Accordingly, a query image is first segmented by color feature, and texture feature can be efficiently extracted from the labeled image of segmentation. Experiments show that the proposed segmentation algorithm achieves high computational speed, and salient regions of images can be effectively extracted. Moreover, compared with the feature extraction method using Discrete Wavelet Transform (DWT), LWT is 15.51 times faster than DWT while keeping the distortion in the retrieval results within a reasonable range.