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.

• Type: Conference
• Year: 2008
• Where: ISM
• Authors: Asadollah Shahbahrami, Ben H. H. Juurlink
• Keywords: Feature Extraction, ISM 2008, Multimedia, Similarity Measurement, Standard Deviation