Publication

Dynamic media content categorisation method

Sabine Süsstrunk, Mahmut Sami Arpa, Gabriel Albert Autes
2020
Patents

Abstract

The present invention concerns a method of classifying a media item into a user profile suitability class in a classification system comprising a set of pre-trained artificial neural networks. The method comprising: receiving (101) a media file; extracting (103) a first data stream and a second data stream from the media file, the first media stream comprising first media content of a first media content form, the second data stream comprising second media content of a second, different media content form, the first and second data streams being comprised in the media item; generating (109, 111, 113) a first sequence of first feature vectors describing the first media content; generating (109, 111, 113) a second sequence of second feature vectors describing the second media content; generating (117) at least a first single feature vector representing the first sequence of first feature vectors and the second sequence of second feature vectors, or generating (117) at least a first single feature vector representing at least the first sequence of first feature vectors, and generating (119) a second feature vector representing at least the second sequence of second feature vectors; generating (123) a probability vector at least from the first single feature vector, or at least from the first and second single feature vectors; and assigning (125) a user profile suitability class to the media item based on the probability vector.

Official source

https://infoscience.epfl.ch/entities/publication/3cda8151-8905-43c5-a2ce-c363c641c308

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Sabine Süsstrunk, Mahmut Sami Arpa, Gabriel Albert Autes
2020
Patents

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/3cda8151-8905-43c5-a2ce-c363c641c308

About this result

Related concepts (25)

Feature learning

In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.

Feature (computer vision)

In computer vision and , a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. Other examples of features are related to motion in image sequences, or to shapes defined in terms of curves or boundaries between different image regions.

Support vector machine

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).

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