The incremental dissimilarity approximations (IDA) algorithm is a recently proposed high-efficient fast image pattern matching algorithm. By splitting the matching vectors

the IDA algorithm saves a lot of pixel-dependment calculations. However

the sub-vectors have a rather weak energy compaction after splitting. This means IDA’s efficiency can further be improved. To avoid the weak energy compaction

sub-vector ordering is proposed

which sorts the sub-vectors by their variances. Candidates would be pruned earlier by the sorted order in pattern matching. Therefore

the average number of unfolded sub-vectors is reduced

which also reducts the search space. Additionally

one more pruning test using the whole vector’s norm before IDA is proposed in our work

and the PDS (partial distortion search) algorithm is introduced in the unfolding sub-vectors step. In our experiment

by testing three types of images in the data sets(indoor scene

natural scene

streetscape)

the overall efficiency of proposed algorithm is improved by 72%~83% compared to the original IDA algorithm.