Abstract: For generating a pictorial reference database from a plurality of digital images, distributed geographical sub-areas are defined (S1). For the geographical sub-areas, selected (S2) from the plurality of digital images are images with a geo-tag located in the respective geographic sub-area. Feature vectors are generated (S31) for localized visual features of the selected images. Based on the feature vectors, matching features are determined (S32) for pairs of the selected images. For pairs with matching features, in each case, a measure of visual similarity is computed (S33) based on different geometric arrangements of the localized visual features. Based on the visual similarity, clusters of images with matching objects are determined (S5) and stored (S6) in the pictorial reference database. The pictorial reference database can be generated automatically from a huge collection of images by dividing the images into geographical subsets which can be processed individually and possibly in parallel.

Abstract: For generating a pictorial reference database from a plurality of digital images, distributed geographical sub-areas are defined (S1). For the geographical sub-areas, selected (S2) from the plurality of digital images are images with a geo-tag located in the respective geographic sub-area. Feature vectors are generated (S31) for localized visual features of the selected images. Based on the feature vectors, matching features are determined (S32) for pairs of the selected images. For pairs with matching features, in each case, a measure of visual similarity is computed (S33) based on different geometric arrangements of the localized visual features. Based on the visual similarity, clusters of images with matching objects are determined (S5) and stored (S6) in the pictorial reference database. The pictorial reference database can be generated automatically from a huge collection of images by dividing the images into geographical subsets which can be processed individually and possibly in parallel.

Abstract: For generating a pictorial reference database from a plurality of digital images, distributed geographical sub-areas are defined (S1). For the geographical sub-areas, selected (S2) from the plurality of digital images are images with a geo-tag located in the respective geographic sub-area. Feature vectors are generated (S31) for localized visual features of the selected images. Based on the feature vectors, matching features are determined (S32) for pairs of the selected images. For pairs with matching features, in each case, a measure of visual similarity is computed (S33) based on different geometric arrangements of the localized visual features. Based on the visual similarity, clusters of images with matching objects are determined (S5) and stored (S6) in the pictorial reference database. The pictorial reference database can be generated automatically from a huge collection of images by dividing the images into geographical subsets which can be processed individually and possibly in parallel.

Abstract: For generating a pictorial reference database from a plurality of digital images, distributed geographical sub-areas are defined (S1). For the geographical sub-areas, selected (S2) from the plurality of digital images are images with a geo-tag located in the respective geographic sub-area. Feature vectors are generated (S31) for localized visual features of the selected images. Based on the feature vectors, matching features are determined (S32) for pairs of the selected images. For pairs with matching features, in each case, a measure of visual similarity is computed (S33) based on different geometric arrangements of the localized visual features. Based on the visual similarity, clusters of images with matching objects are determined (S5) and stored (S6) in the pictorial reference database. The pictorial reference database can be generated automatically from a huge collection of images by dividing the images into geographical subsets which can be processed individually and possibly in parallel.

Abstract: For retrieving information based on images, a first image is taken (S1) using a digital camera associated with a communication terminal (1). Query data related to the first image is transmitted (S3) via a communication network (2) to a remote recognition server (3). In the remote recognition server (3) a reference image is identified (S4) based on the query data. Subsequently, in the remote recognition server (3), a Homography is computed (S5) based on the reference image and the query data, the Homography mapping the reference image to the first image. Moreover, in the remote recognition server (3), a second image is selected (S6) and a projection image is computed (S7) of the second image using the Homography. By replacing a part of the first image with at least a part of the projection image, an augmented image is generated (S8, S10) and displayed (S11) at the communication terminal (1).