Yahoo! Inc.

Oath Inc.

Sriram Jayaprakash Sathish

Subhajit Sanyal

Srinivasan H Sengamedu

Shesha Shah

Prateeksha Uday Chandraghatgi

Malcolm G Slaney

Kilian Quirin Weinberger

Balaji Kannan

Kaushal Kurapati

Polly Ng

A first cluster of web objects is identified from a click-through data structure. The click-through data structure can organize web objects into clusters based on query results of web objects selected by a user. Also, a second cluster of web objects can be identified from a metadata data structure. The metadata data structure can organize web objects into clusters based on metadata associated with the web objects. An output set of web objects is selected, in real time, from the identifier clusters.

Dynamically determining the relatedness of web objects

A search engine determines a set of other images that are similar to a user-selected image, and presents those other images to the user. In determining whether two images are sufficiently similar to each other to merit presentation of one, the search engine determines a Euclidean distance between separate feature vectors that are associated with each of the images. Each such vector indicates diverse types of information that is known about the associated image. The types of information included within such a vector may include attributes that reflect visual characteristics that are visible in an image, verbal tags that have been associated with the image users in a community of users, concepts derived from those tags, coordinates that reflect a geographic location at which a camera that produced the image was when the camera produced the image, and concepts related to groups with which the image is associated.

Image similarity from disparate sources

An example of a method includes determining, electronically, a source location and a destination location in response to an input received through one of a first short message service (SMS) and a general packet radio service (GPRS). The method also includes determining, electronically, a first milestone between the source location and the destination location. Further, the method includes sending directions to reach the first milestone from the source location through a second SMS and an identifier associated with the first milestone. Furthermore, the method includes detecting when to send directions to reach one of a second milestone and the destination location from the first milestone. In addition, the method includes sending directions to reach one of the second milestone and the destination location through a third SMS based on detecting.

Method and system for providing driving directions

Methods and systems are provided that may be used to determine a whether an image contains spam. In one particular embodiment, an image is transformed and spam is detected based on such a transformed image.

Method and system for detecting image spam

The present invention is directed towards systems and methods for filtering unsafe content items within a multimedia repository. The method of the present invention comprises automatically identifying a first plurality of unsafe content items from the content items and automatically identifying a second plurality of unclassifiable content items from the content items. The method identifies a third plurality of unsafe content items from the content items, the third plurality of unsafe content items identified according to a similarity to the first plurality and manually identifying a fourth plurality of unsafe content items, the fourth plurality of unsafe content items comprising a subset of the second plurality of unclassifiable items. A fifth plurality of unsafe content items is then identified, the fifth plurality of unsafe content items identified according to a similarity to the fourth plurality.

System and method for accelerating editorial processes for unsafe content labeling using CBIR

The present invention is directed towards systems and methods for improving image labeling using content-based image retrieval. The method according to one embodiment of the present invention comprises selecting an image query and identifying a first image related to the image query. The method then selects a plurality of second images related to the first image and assigns weights to the plurality of second images. Finally, the method displays the first images and the second images on a client device.

System and method for providing image labeling game using CBIR

A method and apparatus for, in response to receiving a request, retrieving digital images that are associated with one or more keywords and which are similar to a digital image is provided. The request may be processed using a keywords index and a content index, and optionally, a session index that references data about what digital images users have viewed together in a session. A category associated with the identified digital image may also be used in processing the request, e.g., the category associated with the identified digital image may change the weight placed on various content attributes used in comparing the content of the identified digital image to other digital images. After the set of requested digital images are determined, request data, that identifies the set of requested digital images, may be sent to the requestor.

Method and mechanism for retrieving images

A method and apparatus for comparing content attributes, e.g., texture, of one digital image a second digital image. An image operation is performed on gray scale image data to identify intensity transitions of a first digital image. The gray scale image data comprises a set of data values that each identifies a gray scale value. For at least a subset of the set of data values, a slope value for an intensity transition associated with the data value is determined. The slope values are converted to possible slope values of a bounded set of possible slope values. First slope data that describes a frequency of occurrence, across the subset of data values, of each possible slope value is generated. The first slope data is compared to second slope data associated with the second digital image to determine how similar the first digital image is to the second digital image.

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Bengaluru, India

Sriram Jayaprakash Sathish