Image similarity computation using local similarity patterns generated by genetic algorithm

Local similarity pattern (LSP) is proposed as a new method for computing image similarity. Similarity of a pair of images is expressed in terms of similarities of the corresponding image regions, obtained by uniform partitioning of the image area. Different from the conventional methods, each region-wise similarity is computed using a different combination of image features (color, shape, and texture). In addition, a method for optimizing LSP, based on genetic algorithm, is proposed, and incorporated in the relevance feedback process, allowing the user to automatically specify LSP-based queries. LSP is evaluated on four test databases totalling over 2,000 images. Compared with six conventional methods, and SIMPLIcity, an advanced image retrieval system, LSP brings between 15% and 24% increase in the average retrieval precision. LSP, allowing comparison of different image regions using different similarity criteria, is more suited for modeling human perception of image similarity than the conventional methods.