Abstract: Techniques for identifying a salient object with respect to its context are described. A process receives an input image that includes a salient object. The process segments the input image into multiple regions and calculates a saliency value for each of the segmented regions based on scale image levels. The process constructs saliency maps based at least in part on the calculated saliency value, and combines the saliency maps to construct a total saliency map. Next, the process connects a set of line segments computed from the input image and utilizes the total saliency map to compute a closed boundary, which forms a shape prior from the closed boundary, and extracts the salient object from the total saliency map and the shape prior.

Abstract: A method is provided in one example and includes receiving a rules template associated with an online interactive activity by a network element. The rules template includes at least one rule for defining a manner in which a process of the online interactive activity is to be conducted. The method further includes parsing the rules template to determine the at least one rule, and controlling the process of the online interactive activity in accordance with the determined at least one rule.

Abstract: 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.

Abstract: Dominant color names may be extracted from an image by analyzing spatial-context of pixels contained in the image. A dominant color region may be defined by taking a double-threshold approach that addresses ambiguous color regions and a degree of confidence that each pixel belongs in the dominant color region. Affiliation maps and binary maps may be used to generate the dominant color region. Images may be converted to a saliency map, from which a region of interest may be assigned a dominant color name. Image search results may be filtered by the dominant color name associated with the image.

Abstract: A set of data points is divided into a plurality of subsets of data points. A set of cluster closures is generated based at least in part on the subset of data points. Each cluster closure envelopes a corresponding cluster of a set of clusters and is comprised of data points of the enveloped cluster and data points neighboring the enveloped cluster. A k-Means approximator iteratively assigns data points to a cluster of the set of clusters and updates a set of cluster centroids corresponding to the set of clusters. The k-Means approximator assigns data points based at least in part on the set of cluster closures.

Abstract: An input image, which may include a salient object, is received by a salient object detection and localization system. The system may be trained to detect whether the input image includes a salient object. If the system fails to detect a salient object in the input image, the system may provide the sender of the input with a null result or an indication that the input image does not contain a salient object. If the system detects a salient object in the input image, the system may localize the salient object within the input image. The system may generate an output image based at least in part on the localization of the salient object. The system may provide the sender of the input image with information pertaining to the detected salient object.

Abstract: A facility for visual search on a mobile device takes advantage of multi-modal and multi-touch input on the mobile device. By extracting lexical entities from a spoken search query and matching the lexical entities to image tags, the facility provides candidate images for each entity. Selected ones of the candidate images are used to construct a composite visual query image on a query canvas. The relative size and position of the selected candidate images in the composite visual query image, which need not be an existing image, contribute to a definition of a context of the composite visual query image being submitted for context-aware visual search.

Abstract: The techniques described herein determine an initial set of ranked images associated with an image-based search query. Based on visual content similarities between images in the initial set of ranked images, the techniques select confident image samples from the initial set of ranked images. The techniques then use the confident image samples to rerank the initial set of ranked images. Accordingly, a search engine uses the confident image samples to promote images that are likely to be relevant to the search query, while demoting images that are not likely to be relevant to the search query. Therefore, the search engine can provide improved relevance-based search results to an image-based search query.

Abstract: 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.

Abstract: Some implementations include techniques and arrangements to provide document-related representative information with search results. For example, a representative query and/or representative results may be provided for one or more individual documents identified in a set of search results to supplement the search results returned in response to a received search query. The representative queries may be determined by correlating a plurality of previously submitted queries in search log data with a plurality of documents returned in response to the queries. In some implementations, click-through frequency for a particular document with respect to the plurality of queries may be taken into consideration when determining the representative queries for the particular document.

Abstract: This disclosure describes techniques for searching for similar images to an image query by using an approximate k-Nearest Neighbor (k-NN) graph. The approximate k-NN graph is constructed from data points partitioned into subsets to further identify nearest-neighboring data points for each data point. The data points may connect with the nearest-neighboring data points in a subset to form an approximate neighborhood subgraph. These subgraphs from all the subsets are combined together to form a base approximate k-NN graph. Then by performing more random hierarchical partition, more base approximate k-NN graphs are formed, and further combined together to create an approximate k-NN graph. The approximate k-NN graph expands into other neighborhoods and identifies the best k-NN data points. The approximate k-NN graph retrieves the best NN data points, based at least in part on the retrieved best k-NN data points representing images being similar in appearance to the image query.

Abstract: 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.

Abstract: An image-based text extraction and searching system extracts an image be selected by gesture input by a user and the associated image data and proximate textual data in response to the image selection. Extracted image data and textual data can be utilized to perform or enhance a computerized search. The system can determine one or more database search terms based on the textual data and generate at least a first search query proposal related to the image data and the textual data.

Abstract: Dominant color names may be extracted from an image by analyzing spatial-context of pixels contained in the image. A dominant color region may be defined by taking a double-threshold approach that addresses ambiguous color regions and a degree of confidence that each pixel belongs in the dominant color region. Affiliation maps and binary maps may be used to generate the dominant color region. Images may be converted to a saliency map, from which a region of interest may be assigned a dominant color name. Image search results may be filtered by the dominant color name associated with the image.

Abstract: 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.

Abstract: This disclosure describes techniques for searching for similar images to an image query by using an approximate k-Nearest Neighbor (k-NN) graph. The approximate k-NN graph is constructed from data points partitioned into subsets to further identify nearest-neighboring data points for each data point. The data points may connect with the nearest-neighboring data points in a subset to form an approximate neighborhood subgraph. These subgraphs from all the subsets are combined together to form a base approximate k-NN graph. Then by performing more random hierarchical partition, more base approximate k-NN graphs are formed, and further combined together to create an approximate k-NN graph. The approximate k-NN graph expands into other neighborhoods and identifies the best k-NN data points. The approximate k-NN graph retrieves the best NN data points, based at least in part on the retrieved best k-NN data points representing images being similar in appearance to the image query.

Abstract: Techniques for identifying a salient object with respect to its context are described. A process receives an input image that includes a salient object. The process segments the input image into multiple regions and calculates a saliency value for each of the segmented regions based on scale image levels. The process constructs saliency maps based at least in part on the calculated saliency value, and combines the saliency maps to construct a total saliency map. Next, the process connects a set of line segments computed from the input image and utilizes the total saliency map to compute a closed boundary, which forms a shape prior from the closed boundary, and extracts the salient object from the total saliency map and the shape prior.

Abstract: 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.

Abstract: 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.

Abstract: 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.