Conference article

HLAC (Higher Order Local Autocorrelation) features are popular image descriptors that have been used for various image-processing applications since the 1980s. Examples of the application of the HLAC features include KANSEI retrievals and subjective retrievals of 2D image databases. In this paper; standard HLAC masks are extended for computing a massive number of features. Typical HLAC features are computed by applying 25 masks to a binary image; whereas our Ext-HLAC features are computed by applying 16;241;567 masks. Since there are a high number of mask combinations; we have developed Ext-HLAC mask generation software programs. Ext-HLAC masks were tested by using 2D benchmark image database sets. For each image; the pattern features were extracted by applying Ext-HLAC masks; and the pattern features were analyzed by a k-NN based approach. Our preliminary experiments show high classification rates for certain image databases.

HLAC; Ext-HLAC; pattern feature; k-NN; image classification