Abstract: Classification of image regions comprises: recursively partitioning an image into a tree of image regions having the image as a tree root and at least one image patch in each leaf image region of the tree, the tree having nodes defined by the image regions and edges defined by pairs of nodes connected by edges of the tree; assigning unary classification potentials to nodes of the tree; assigning pairwise classification potentials to edges of the tree; and labeling the image regions of the tree of image regions based on optimizing an objective function comprising an aggregation of the unary classification potentials and the pairwise classification potentials.

Abstract: An apparatus, method, and computer program product are provided for generating an image representation. The method includes receiving an input digital image, extracting features from the image which are representative of patches of the image, generating weighting factors for the features based on location relevance data for the image, and weighting the extracted features with the weighting factors to form a representation of the image.

Abstract: A first graph embedded in a Euclidean space is modeled by a globally rigid first model graph that includes all vertices and edges of the first graph and has a preselected maximum clique size. The modeling is configured to maintain the preselected maximum clique size by employing an edge adding process that replicates a vertex of a vertex pair connected by an edge. A mapping between vertices of the first graph and vertices of a second graph is computed by optimizing a mapping between vertices of the first model graph and vertices of the second graph.

Abstract: A first graph embedded in a Euclidean space is modeled by a globally rigid first model graph that includes all vertices and edges of the first graph and has a preselected maximum clique size. The modeling is configured to maintain the preselected maximum clique size by employing an edge adding process that replicates a vertex of a vertex pair connected by an edge. A mapping between vertices of the first graph and vertices of a second graph is computed by optimizing a mapping between vertices of the first model graph and vertices of the second graph.

Abstract: Classification of image regions comprises: recursively partitioning an image into a tree of image regions having the image as a tree root and at least one image patch in each leaf image region of the tree, the tree having nodes defined by the image regions and edges defined by pairs of nodes connected by edges of the tree; assigning unary classification potentials to nodes of the tree; assigning pairwise classification potentials to edges of the tree; and labeling the image regions of the tree of image regions based on optimizing an objective function comprising an aggregation of the unary classification potentials and the pairwise classification potentials.

Abstract: An apparatus, method, and computer program product are provided for generating an image representation. The method includes receiving an input digital image, extracting features from the image which are representative of patches of the image, generating weighting factors for the features based on location relevance data for the image, and weighting the extracted features with the weighting factors to form a representation of the image.