Visenze Pte Ltd

National University of Singapore

Guangda Li

Zheng Song

Fan Xu

Meng Wang

Tat Seng Chua

Zheng Lu

Thanh Tung Todd Cao

Xin Ji

Zhenhua Wang

A method of generating an image recognition model for recognising an input image and a system thereof are provided. The method includes appending at least one feature extraction layer to the image recognition model, extracting a plurality of feature vectors from a set of predetermined images, grouping the plurality of feature vectors into a plurality of categories, clustering the plurality of feature vectors of each of the plurality of categories into at least one cluster, determining at least one centroid for each of the at least one cluster, such that each of the at least one cluster comprises at least one centroid, such that each of the at least one centroid is represented by a feature vector, generating a classification layer based on the feature vector of the at least one centroid of the plurality of categories, and appending the classification layer to the image recognition model. In addition, a method of classifying an input image and a system thereof are provided.

System and a method for generating an image recognition model and classifying an input image

According to various embodiments, the present invention provides a product indexing method including receiving product data of a product, the product data comprising at least an image, such that the image includes a product image of the product and a context image which provides the context that the product may be in, such that the context image includes at least one non-product image. Product indexing method further includes identifying the product image, identifying the context image from the image, verifying the product in the product image based on the context image, extracting the product image and indexing the product image. According to various embodiment, the present invention provides a product indexing system for the product indexing system.

Product indexing method and system thereof

Present example embodiments relate generally to systems, devices, and methods for managing video content. The method may include receiving an input video having one or more video frames. The method may also include selecting a video frame from the input video that displays a target product and/or other feature. The method may also include generating an alternative representation of the target product and/or other feature displayed in the selected video frame. The method may also include searching a content database for media content that is likely to pertain to the target product and/or other feature displayed in the selected video frame. The searching of the content database may be performed using the alternative representation of the target product and/or other feature. The method may also include associating a most likely matching media content found, in the searching, to the selected video frame.

Method and system for identifying relevant media content

Media content relevant to an input video is identified by analyzing video frames in the input video to detect if any of the video frames contain a target product. One or more video frames found to be containing a detected target product is selected and a product thumbnail is generated for each of selected the video frames. At least one product thumbnails is selected and a video product visual index is generated for each of the selected product thumbnail. Relevant media content is then identified for each of the product thumbnails by comparing the video product visual index of each of the selected product thumbnail with a plurality of media content visual index in a media content database. Each media content visual index is associated with one media content in the media content product database.

A system provides product annotation in a video to one or more users. The system receives a video from a user, where the video includes multiple video frames. The system extracts multiple key frames from the video and generates a visual representation of the key frame. The system compares the visual representation of the key frame with a plurality of product visual signatures, where each visual signature identifies a product. Based on the comparison of the visual representation of the key frame and a product visual signature, the system determines whether the key frame contains the product identified by the visual signature of the product. To generate the plurality of product visual signatures, the system collects multiple training images comprising multiple of expert product images obtained from an expert product repository, each of which is associated with multiple product images obtained from multiple web resources.

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Singapore, Singapore

Guangda Li