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Texture Image Classification Using Extended 2D HLAC Features
| Content Provider | Semantic Scholar |
|---|---|
| Author | Suzuki, Motofumi T. |
| Copyright Year | 2014 |
| Abstract | 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. |
| Starting Page | 1093 |
| Ending Page | 1102 |
| Page Count | 10 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://dqi.id.tue.nl/keer2014/papers/KEER2014_101 |
| Alternate Webpage(s) | http://www.ep.liu.se/ecp/100/091/ecp14100091.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
