Optimization of Content-Based Image Retrieval Functions

Feature extraction and similarity measurement are two important operations in content-based image retrieval systems. We optimize and vectorize typical feature extraction algorithms, mean and standard deviation, and some similarity measurement functions such as the sum-of-squared-differences (SSD), the sum-of-absolute differences (SAD), and histogram intersection on a general-purpose processor enhanced with SIMD extensions. In the straightforward implementation of the mean and standard deviation, there are two passes, one to compute the mean and one to compute the standard deviation.We use a single-loop approach that computes both the mean and the standard deviation in a single pass. This technique yields a speedup of up to 1.85 over the double-loop implementation. We vectorize the single-loop implementation using the MMX and SSE2 extensions. The vectorized versions improve performance by a factor of up to 14.49. In addition,we vectorize the SSD, SAD, and histogram intersection similarity measurements using SSE. The vectorized versions provide a maximum speedup of 1.45, 2.33, and 5.24 for the SSD, the SAD, and histogram intersection, respectively,over the optimized scalar implementations.