BASIL: Effective Near-Duplicate Image Detection Using Gene Sequence Alignment

Finding near-duplicate images is a task often found in Multimedia Information Retrieval (MIR). Toward this effort; we propose a novel idea by bridging two seemingly unrelated fields MIR and Biology That is, we propose to use the popular gene sequence alignment algorithm in Biology, i.e.. BLAST, in detecting near-duplicate images. Under the new idea, we study how various image features and gene sequence generation methods (using gene alphabets such as A, C. G. and T in DNA sequences) affect the accuracy and performance of detecting near-duplicate images. Our proposal, termed as BLASTed Image Linkage (BASIL), is empirically validated using various real data sets. This work can lie viewed as the "first" step toward bridging MIR and Biology fields in the well-studied near-duplicate image detection problem.