Microsoft Technology Licensing, LLC

Linjun Yang

Xian-Sheng Hua

Shipeng Li

Meng Wang

Arun Sacheti

Tao Mei

Bo Geng

Qi Tian

Yang Cai

Hong-Jiang Zhang

Bin Benjamin Zhu

Rui Guo

Yang Yang

Houdong Hu

Li Huang

Meenaz Merchant

Lei Wu

Bingbing Ni

Fei Wang

Xi Chelsea Chen

Rui Liu

Yan Wang

Dong Hui Liu

Jingdong Wang

Ming Ye

Karim Hasham

Kuang-Huei Lee

Saurajit Mukherjee

Ye Wu

Zhengjun Zha

Darui Li

Xinmei Tian

Pavel Komlev

Bo Liu

Yokesh Kumar

Tianjun Xiao

Jiapei Huang

Lifeng Shang

The description relates to diversified hybrid image annotation for annotating images. One implementation includes generating first image annotations for a query image using a retrieval-based image annotation technique. Second image annotations can be generated for the query image using a model-based image annotation technique. The first and second image annotations can be integrated to generate a diversified hybrid image annotation result for the query image.

Image annotation

Systems and methods can be implemented to conduct searches based on images used as queries in a variety of applications. In various embodiments, a set of visual words representing a query image are generated from features extracted from the query image and are compared with visual words of index images. A set of candidate images is generated from the index images resulting from matching one or more visual words in the comparison. A multi-level ranking is conducted to sort the candidate images of the set of candidate images, and results of the multi-level ranking are returned to a user device that provided the query image. Additional systems and methods are disclosed.

Multi-modal visual search pipeline for web scale images

Systems, computing devices, and methods for performing an image search are presented. A search query including an image is received from a user. A segment associated with the image is identified. A user intent associated with the image and the segment is identified. Search results associated with the identified segment and user intent are generated, and presented to the user.

Task-focused search by image

Techniques describe submitting a video clip as a query by a user. A process retrieves images and information associated with the images in response to the query. The process decomposes the video clip into a sequence of frames to extract the features in a frame and to quantize the extracted features into descriptive words. The process further tracks the extracted features as points in the frame, a first set of points to correspond to a second set of points in consecutive frames to construct a sequence of points. Then the process identifies the points that satisfy criteria of being stable points and being centrally located in the frame to represent the video clip as a bag of descriptive words for searching for images and information related to the video clip.

Searching for images by video

Technologies for a human computation framework suitable for answering common sense questions that are difficult for computers to answer but easy for humans to answer. The technologies support solving general common sense problems without a priori knowledge of the problems; support for determining whether an answer is from a bot or human so as to screen out spurious answers from bots; support for distilling answers collected from human users to ensure high quality solutions to the questions asked; and support for preventing malicious elements in or out of the system from attacking other system elements or contaminating the solutions produced by the system, and preventing users from being compensated without contributing answers.

Comprehensive human computation framework

Adaptive image retrieval image allows retrieval of images that are more likely to reflect a current trend of user preferences and/or interests, and therefore can provide relevant results to an image search. Adaptive image retrieval includes receiving image query log data from one or more clients, and updating a codebook of features based on the received query log data. The image query log data includes images that have been queried by the one or more clients within a predetermined period of time.

Adaptive image retrieval database

A similarity of a first video to a second video may be identified automatically. Images are received from the videos, and divided into sub-images. The sub-images are evaluated based on a feature common to each of the sub-images. Binary representations of the images may be created based on the evaluation of the sub-images. A similarity of the first video to the second video may be determined based on a number of occurrences of a binary representation in the first video and the second video.

Near-duplicate video retrieval

This document describes techniques that utilize a learning method to generate a ranking model for use in image search systems. The techniques leverage textual information and visual information simultaneously when generating the ranking model. The tools are further configured to apply the ranking model responsive to receiving an image search query.

Content-aware ranking for visual search

An ImageWiki architecture is used to generate an image-based web page for an image on the Web. An ImageWiki page may be created automatically or individually, by a user of the Web. Additionally, a user may revise existing ImageWiki pages to update a particular page or correct an incorrect or misleading previous entry. The ImageWiki application indexes images located on the Web. Once the images are indexed, the information related to the images is mined and extracted from various sources of web data. Finally, an ImageWiki page or web page is generated for each image. The resulting ImageWiki page contains the image as well as the aggregated information relating to the image.

Creating and modifying an image wiki page

Technologies for recommending relevant tags for the tagging of media based on one or more initial tags provided for the media and based on a large quantity of other tagged media. Sample media as candidates for recommendation are provided by a set of weak rankers based on corresponding relevance measures in semantic and visual domains. The various samples provided by the weak rankers are then ranked based on relative order to provide a list of recommended tags for the media. The weak rankers provide sample tags based on relevance measures including tag co-occurrence, tag content correlation, and image-conditioned tag correlation.

Media tag recommendation technologies

Systems and methods for determining insertion points in a first video stream are described. The insertions points being configured for inserting at least one second video into the first video. In accordance with one embodiment, a method for determining the insertion points includes parsing the first video into a plurality of shots. The plurality of shots includes one or more shot boundaries. The method then determines one or more insertion points by balancing a discontinuity metric and an attractiveness metric of each shot boundary.

Advertisement insertion points detection for online video advertising

Some implementations provide techniques and arrangements to perform image retrieval. For example, some implementations identify an object of interest and a visual context in a first image. In some implementations, a second image that includes a second object of interest and a second visual context may be compared to the object of interest and the visual content, respectively, to determine whether the second image matches the first image.

Object retrieval using visual query context

Colors of images and videos are modified to make differences in the colors more perceptible to colorblind users. An exemplary recoloring process utilizes a color space transformation, a local color rotation and a global color rotation to transform colors of visual objects from colors which may not be distinguishable by the colorblind user to colors which may be distinguishable by the colorblind user.

Efficient image and video recoloring for colorblindness

Supervised re-ranking for visual search may include re-ordering images that are returned in response to a text-based image search by exploiting visual information included in the images. In one example, supervised re-ranking for visual search may include receiving a textual query, obtaining an initial ranking result including a plurality of images corresponding to the textual query, and representing the textual query by a visual context of the plurality of images. A query-independent re-ranking model may be trained based on visual re-ranking features of the plurality of images of the textual query in accordance with a supervised training algorithm.

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Beijing, China

Linjun Yang