摘要:
Described is a technology for computing visual and textual summaries for tagged image collections. Heterogeneous affinity propagation is used to together identify both visual and textual exemplars. The heterogeneous affinity propagation finds the exemplars for relational heterogeneous data (e.g., images and words) by considering the relationships (e.g., similarities) within pairs of images, pairs of words, and relationships of words to images (affinity) in an integrated manner.
摘要:
Techniques for constructing an optimized kd-tree are described. In an implementation, an optimized kd-tree process receives input of a set of data points applicable for large-scale computer vision applications. The process divides the set of the data points into subsets of data points with nodes while generating hyperplanes (e.g., coordinate axes). The process identifies a partition axis for each node based on the coordinate axes combined in a binary way. The optimized kd-tree process creates an optimized kd-tree that organizes the data points based on the identified partition axis. The organization of the data points in the optimized kd-tree provides efficient indexing and searching for a nearest neighbor.
摘要:
This disclosure describes various exemplary user interfaces, methods, and computer program products for the interactively ranking image search results refinement method using a color layout. The method includes receiving a text query for an image search, presenting image search results in a structured presentation based on the text query and information from an interest color layout. The process creates image search results that may be selected by the user based on color selection palettes or color layout specification schemes. Then the process ranks the image search results by sorting the results according to similarity scores between color layouts from the image search results and the interest color layout from a user based on the color selection palettes and the color layout specification schemes.
摘要:
Techniques for intelligent image search results summarization and browsing scheme are described. Images having visual attributes are evaluated for similarities based in part on their visual attributes. At least one preference score indicating a probability of an image to be selected into a summary is calculated for each image. Images are selected based on the similarity of the selected images to the other images and the preference scores of the selected images. A summary of the plurality of images is generated including the selected one individual image.
摘要:
Techniques for constructing an optimized kd-tree are described. In an implementation, an optimized kd-tree process receives input of a set of data points applicable for large-scale computer vision applications. The process divides the set of the data points into subsets of data points with nodes while generating hyperplanes (e.g., coordinate axes). The process identifies a partition axis for each node based on the coordinate axes combined in a binary way. The optimized kd-tree process creates an optimized kd-tree that organizes the data points based on the identified partition axis. The organization of the data points in the optimized kd-tree provides efficient indexing and searching for a nearest neighbor.
摘要:
Techniques for intelligent image search results summarization and browsing scheme are described. Images having visual attributes are evaluated for similarities based in part on their visual attributes. At least one preference score indicating a probability of an image to be selected into a summary is calculated for each image. Images are selected based on the similarity of the selected images to the other images and the preference scores of the selected images. A summary of the plurality of images is generated including the selected one individual image.
摘要:
Techniques for intelligent image search results summarization and browsing scheme are described. Images having visual attributes are evaluated for similarities based in part on their visual attributes. At least one preference score indicating a probability of an image to be selected into a summary is calculated for each image. Images are selected based on the similarity of the selected images to the other images and the preference scores of the selected images. A summary of the plurality of images is generated including the selected one individual image.
摘要:
The concept-structured image search technique described herein pertains to a technique for enabling a user to indicate their semantic intention and then retrieve and rank images from a database or other image set according to this intention. The concept-structured image search technique described herein includes a new interface for image search. With this interface, a user can freely type several key textual words in arbitrary positions on a blank image, and also describe a region for each keyword that indicates its influence scope, which is called concept structure herein. The concept-structured image search technique will return and rank images that are in accordance with the concept structure indicated by the user. One embodiment of the technique can be used to create a synthesized image without actually using the synthesized image to perform a search of an image set.
摘要:
A hybrid search method may be used to identify information responsive to a query. A search may be performed utilizing a neighborhood graph and a partitioning tree. The partitioning tree may be searched to select one or more pivots that may be used to guide a subsequent search in the neighborhood graph. Once the search in the neighborhood graph is unable to identify nearest neighbors in closer proximity to the query, the search may be switched to the partitioning tree. The partitioning tree may then be searched to select pivots that may be used to guide subsequent searches in the neighborhood graph. The searches performed in the partitioning tree and/or the neighborhood graph may be conducted utilizing an iterative algorithm.
摘要:
A hybrid search method may be used to identify information responsive to a query. A search may be performed utilizing a neighborhood graph and a partitioning tree. The partitioning tree may be searched to select one or more pivots that may be used to guide a subsequent search in the neighborhood graph. Once the search in the neighborhood graph is unable to identify nearest neighbors in closer proximity to the query, the search may be switched to the partitioning tree. The partitioning tree may then be searched to select pivots that may be used to guide subsequent searches in the neighborhood graph. The searches performed in the partitioning tree and/or the neighborhood graph may be conducted utilizing an iterative algorithm.