Gracenote, Inc.

Roku, Inc.

Zeitera, LLC

Sunil Suresh Kulkarni

Jose Pio Pereira

Shashank C Merchant

Mihailo M Stojancic

Prashant Ramanathan

Peter Wendt

Pradipkumar Dineshbhai Gajjar

Oleksiy Bolgarov

Kyle Brocklehurst

Preshant Ramanathan

Accurately detection of logos in media content on media presentation devices is addressed. Logos and products are detected in media content produced in retail deployments using a camera. Logo recognition uses saliency analysis, segmentation techniques, and stroke analysis to segment likely logo regions. Logo recognition may suitably employ feature extraction, signature representation, and logo matching. These three approaches make use of neural network based classification and optical character recognition (OCR). One method for OCR recognizes individual characters then performs string matching. Another OCR method uses segment level character recognition with N-gram matching. Synthetic image generation for training of a neural net classifier and utilizing transfer learning features of neural networks are employed to support fast addition of new logos for recognition.

Logo recognition in images and videos

Audio distortion compensation methods to improve accuracy and efficiency of audio content identification are described. The method is also applicable to speech recognition. Methods to detect the interference from speakers and sources, and distortion to audio from environment and devices, are discussed. Additional methods to detect distortion to the content after performing search and correlation are illustrated. The causes of actual distortion at each client are measured and registered and learnt to generate rules for determining likely distortion and interference sources. The learnt rules are applied at the client, and likely distortions that are detected are compensated or heavily distorted sections are ignored at audio level or signature and feature level based on compute resources available. Further methods to subtract the likely distortions in the query at both audio level and after processing at signature and feature level are described.

Robust audio identification with interference cancellation

A mobile device responds in real time to media content presented on a media device, such as a television. The mobile device captures temporal fragments of audio-video content on its microphone, camera, or both and generates corresponding audio-video query fingerprints. The query fingerprints are transmitted to a search server located remotely or used with a search function on the mobile device for content search and identification. Audio features are extracted and audio signal global onset detection is used for input audio frame alignment. Additional audio feature signatures are generated from local audio frame onsets, audio frame frequency domain entropy, and maximum change in the spectral coefficients. Video frames are analyzed to find a television screen in the frames, and a detected active television quadrilateral is used to generate video fingerprints to be combined with audio fingerprints for more reliable content identification.

Media content identification on mobile devices

Techniques are described that exclude use of 'stop-fingerprints' from media database formation and search query to an automatic content recognition (ACR) systems based on media content fingerprints updated by stop-fingerprint analysis. A classification process is presented which takes in fingerprints from reference media files as an input and produces a modified set of fingerprints as an output by applying a novel stop-fingerprint classification algorithm. Architecture for the distributed stop-fingerprint generation is presented. Various cases, as stop-fingerprints generation for the entire reference database, stop-fingerprints generation for the individual reference fingerprint files, and temporal fingerprint classification obtained through intermediate steps of the temporal fingerprint classification algorithm are presented. A hash-based signature classification algorithm is also described.

Method to differentiate and classify fingerprints using fingerprint neighborhood analysis

Methods, apparatus, systems and articles of manufacture of logo recognition in images and videos are disclosed. An example method to detect a specific brand in images and video streams comprises accepting luminance images at a scale in an x direction Sx and a different scale in a y direction Sy in a neural network, and training the neural network with a set of training images for detected features associated with a specific brand.

The overall architecture and details of a scalable video fingerprinting and identification system that is robust with respect to many classes of video distortions is described. In this system, a fingerprint for a piece of multimedia content is composed of a number of compact signatures, along with traversal hash signatures and associated metadata. Numerical descriptors are generated for features found in a multimedia clip, signatures are generated from these descriptors, and a reference signature database is constructed from these signatures. Query signatures are also generated for a query multimedia clip. These query signatures are searched against the reference database using a fast similarity search procedure, to produce a candidate list of matching signatures. This candidate list is further analyzed to find the most likely reference matches. Signature correlation is performed between the likely reference matches and the query clip to improve detection accuracy.

Media fingerprinting and identification system

An architecture for a multimedia search system is described. To perform similarity matching of multimedia query frames against reference content, reference database comprising of a cluster index using cluster keys to perform similarity matching and a multimedia index to perform sequence matching is built. Methods to update and maintain the reference database that enables addition and removal of the multimedia contents, including portions of multimedia content, from the reference database in a running system are described. Hierarchical multi-level partitioning methods to organize the reference database are presented. Smart partitioning of the reference multimedia content according to the nature of the multimedia content, and according to the popularity among the social media, that supports scalable fast multimedia identification is also presented. A caching mechanism for multimedia search queries in a centralized or in a decentralized distributed system and a client based local multimedia search system enabling multimedia tracking are described.

Scalable, adaptable, and manageable system for multimedia identification

Audio distortion compensation methods to improve accuracy and efficiency of audio content identification are described. The method is also applicable to speech recognition. Methods to detect the interference from speakers and sources, and distortion to audio from environment and devices are discussed. Additional methods to detect distortion to the content after performing search and correlation are illustrated. The causes of actual distortion at each client are measured and registered and learnt to generate rules for determining likely distortion and interference sources. The learnt rules are applied at the client, and likely distortions that are detected are compensated or heavily distorted sections are ignored at audio level or signature and feature level based on compute resources available. Further methods to subtract the likely distortions in the query at both audio level and after processing at signature and feature level are described.

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Santa Cruz, CA, United States of America

Sunil Suresh Kulkarni