Abstract: Methods, devices, and computer-readable media of task execution based on a knowledge graph obtained by entity alignment are provided. In one aspect, a method includes: obtaining a knowledge graph pair including a first knowledge graph and a second knowledge graph, selecting a target entity by filtering entities in the first knowledge graph, determining a centrality and an uncertainty of the target entity based on the target entity, an entity corresponding to a neighboring node, and entities in the second knowledge graph, constructing a sample entity pair based on the centrality and the uncertainty of the target entity, training a to-be-trained entity alignment model using the sample entity pair, performing entity alignment on to-be-aligned knowledge graphs according to the trained entity alignment model to obtain a merged knowledge graph, and executing a target task based on the merged knowledge graph.

Abstract: The present disclosure discloses a method and apparatus for creating a sample image index table, filtering image, and searching image, to improve accuracy of monitoring images. A method for image filtering comprises: establishing a sample image index table; extracting regional characteristics from an image to be searched; clustering the regional characteristics of the image to be searched into corresponding nodes; obtaining a corresponding sample image identification by indexing the sample image index table using node identifications of the nodes of the image to be searched; determining a number of duplicate nodes between the image to be searched and the sample image; obtaining a degree of similarity of the image to be searched based on a number of the nodes of the image to be searched and a number of the nodes of the sample image; and filtering out the image to be searched when a degree of similarity between the image to be searched and the sample image exceeds a similarity threshold.

Abstract: The present disclosure discloses a method and apparatus for creating a sample image index table, filtering image, and searching image, to improve accuracy of monitoring images. A method for image filtering comprises: establishing a sample image index table; extracting regional characteristics from an image to be searched; clustering the regional characteristics of the image to be searched into corresponding nodes; obtaining a corresponding sample image identification by indexing the sample image index table using node identifications of the nodes of the image to be searched; determining a number of duplicate nodes between the image to be searched and the sample image; obtaining a degree of similarity of the image to be searched based on a number of the nodes of the image to be searched and a number of the nodes of the sample image; and filtering out the image to be searched when a degree of similarity between the image to be searched and the sample image exceeds a similarity threshold.

Abstract: Described is a technology in which image near-duplicate retrieval is performed using similarities between patterns of query image words and patterns of database image words. In general, the image retrieval problems resulting from visual polysemy are reduced by using such visual patterns. Visual word vectors and visual pattern vectors are determined for the query image and a database image. These four vectors are used to determine similarity between the database image and the query image. The similarity scores may be used for ranking and/or re-ranking the database image similarity to the query image relative to other database images' similarity scores. Also described is expanding a query visual word of the query image to a set of visual words that are visual synonyms with the query visual word, to help reduce image retrieval problems resulting from visual synonymy.

Abstract: Described is a technology in which image near-duplicate retrieval is performed using similarities between patterns of query image words and patterns of database image words. In general, the image retrieval problems resulting from visual polysemy are reduced by using such visual patterns. Visual word vectors and visual pattern vectors are determined for the query image and a database image. These four vectors are used to determine similarity between the database image and the query image. The similarity scores may be used for ranking and/or re-ranking the database image similarity to the query image relative to other database images' similarity scores. Also described is expanding a query visual word of the query image to a set of visual words that are visual synonyms with the query visual word, to help reduce image retrieval problems resulting from visual synonymy.

Abstract: Method for determining a relevance between an image and information pertaining to the image, including receiving information about a geographical region, displaying the image related to the geographical region, receiving an estimated location information of the image, and determining the relevance between the image and the information based on the estimated location information.