Microsoft Technology Licensing, LLC

Maayan Yedidia

Yonit Hoffman

Zvi Figov

Mattan Serry

Oron Nir

Avner Levi

Inbal Sagiv

Fardau Van Neerden

Itai Norman

Tom Hirshberg

Systems, methods, and a computer-readable medium are provided for matching textless elements to texted elements in video content. A video processing system including a textless matching system may divide a video into shots, identify shots having similar durations, identify sequences of shots having similar durations, and compare image content in representative frames of the sequences to determine whether the sequences match. When the sequences are determined to match, the sequences may be paired, wherein the first sequence may include shots with overlaid text and the second sequence may include textless version of corresponding texted shots included in the first sequence. In some examples, the video processing system may further replace the determined corresponding texted shots.

Textless material scene matching in videos

Systems and methods for spatial-textual clustering-based recognition of text in videos are disclosed. A method includes performing textual clustering on a first subset of a set of predictions that correspond to numeric characters only and performing spatial-textual clustering on a second subset of the set of predictions that correspond to alphabetical characters only. The method includes, for each cluster of predictions associated with the first subset of the set of predictions, choosing a first cluster representative to correct any errors in each cluster of predictions associated with the first subset of the set of predictions and outputting any recognized numeric characters. The method includes, for each cluster of predictions associated with the second subset of the set of predictions, choosing a second cluster representative to correct any errors in each cluster of predictions associated with the second subset of the set of predictions and outputting any recognized alphabetical characters.

Clustering-based recognition of text in videos

A video-processing technique uses machine-trained logic to detect and track people that appear in video information. The technique then ranks the prominence of these people in the video information, to produce ranking information. The prominence of a person reflects a level of importance of the person in the video information, corresponding to the capacity of the person to draw the attention of a viewer. For instance, the prominence of the person reflects, at least in part, an extent to which the person appears in the video information. The technique performs its ranking based on person-specific feature information. The technique produces each instance of person-specific feature information by accumulating features pertaining to a particular person. One or more application systems make use of the ranking information to control the presentation of the video information.

Detecting prominence of objects in video information

Examples of the present disclosure describe systems and methods for multichannel audio speech classification. In examples, an audio signal comprising multiple audio channels is received at a processing device. Each of the audio channels in the audio signal is transcoded to a predefined audio format. For each of the transcoded audio channels, an average power value is calculated for one or more data windows in the audio signal. A correlation value is calculated between the average power value for each audio channel and the combined average power value of the other audio channels in the audio signal. Each of the correlation values (or an aggregated correlation value for the audio channels) is then compared against a threshold value to determine whether the audio signal is to be classified as a speech-based communication. Based on the classification, an action associated with the audio signal may be performed.

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Ramat Gan, Israel

Maayan Yedidia