A clustering method and apparatus using region division templates are provided. The method includes: dividing a photo into regions by using region division templates; modeling a semantic concept included in a divided region; when it is assumed that a measured value indicating the degree that the image of the region includes a semantic concept corresponding to the region is a confidence degree, merging the semantic concepts of respective regions with respect to the confidence degree of the local meaning measured from the modeling; modeling a global semantic concept included in the photo by using a final local semantic concept determined after the merging; and determining one or more categories included in the input photo according to the confidence degree of the global semantic concept measured from the modeling. According to the method and apparatus, in order to more reliably extract semantic concepts included in a photo, multiple content-based feature values are extracted from region images divided by using region division templates, and the confidence degree of an input image in relation to the local semantic concept defined by using the feature values is measured. With respect to the confidence degree, the local semantic concepts of the photo are merged and a more reliable local semantic concept is extracted. By using the merged local semantic concept, the confidence degree of a global semantic concept is measured, and according to the confidence, multiple category concepts included in the input photo are extracted. By doing so, photo data can be quickly and effectively used to generate an album.
METHOD AND APPARATUS FOR CATEGORY-BASED CLUSTERING USING PHOTOGRAPHIC REGION TEMPLATES OF DIGITAL PHOTO