In this paper we propose a novel feature based on SIFT (Scale Invariant Feature Transform) algorithm1 forthe robust representation of local visual contents. SIFT features have raised much interest for their power ofdescription of visual content characterizing punctual information against variation of luminance and change of viewpoint and they are very useful to capture local information. For a single image hundreds of key points are found and they are particularly suitable for tasks dealing with image registration or image matching. In this work we stretched the spatial coverage of descriptors creating a novel feature as composition of key points present in an image region while maintaining the invariance properties of SIFT descriptors. The number of descriptorsis reduced, limiting the computational weight, and at the same time a more abstract descriptor is achieved. The new feature is therefore suitable at describing objects and characteristic image regions.We tested the retrieval performance with a dataset used to test PCA SIFT2 and image matching capabilityamong images processed with affine transformations. Experimental results are reported.
|Numero di pagine||7|
|Stato di pubblicazione||Published - 2010|
All Science Journal Classification (ASJC) codes