Mining for video production invariants to measure style similarity

Haidar, Siba; Joly, Philippe; Chebaro, Bilal

doi:10.1002/int.20158

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…The homogeneity property of features was also used by Haidar et al in order to segment audiovisual documents using similarity matrices [26]. The idea of the work is to measure the similarity between documents based on some styles [35] and has not as main aim to segment TV streams. The similarity measure can be applied in order to detect near-duplicate videos, to measure how much two videos are similar or to detect similar segments between two videos.…”

Section: Program-based Methodsmentioning

confidence: 99%

TV Stream Table of Content: A New Level in the Hierarchical Video Representation

Ibrahim¹

2018

JCSA

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With the rapid development of nowadays technologies, TV could keep its position as one of the most important entertainment and sometimes educative utilities in our daily life. However, keeping this position required a lot of major changes to take place in order for the TV to follow up with the digital revolution, such as, digital broadcasting, High Definition TV, TV on demand. TV-REPLAY, WebTV, etc. This evolution accompanied with many other factors such as the vast spread of communication means and the low prices of storing media have all resulted in many other indispensable technologies for video content storing, structuring, searching and retrieval. Video content can be of various types: a sequence of frames, a sequence of shots, a sequence of scenes, or a sequence of programs which is what the TV stream is usually composed of. Video content structuring would be of a great benefit to help indexing searching and retrieving information from the content efficiently. For example, structuring a soccer game into Play/Break phases facilitates later the detection of goals or summarizing the soccer video. Another example is to structure a news program into stories where each story is composed of an anchorperson segment followed by a report, which facilitates later the search of a specific story or an intelligent navigation inside the news program. However, all the existing analysis methods are dedicated for one type of video content. Such methods generate very poor results if it is applied on a TV stream that is composed of several video programs. So, it is important to detect a priori the boundaries of each program and then identify the type of each program in order to run the dedicated analysis method based on the type. For a TV viewer, a TV stream is a sequence of programs (P) and breaks (B). Programs may be separated by breaks and may include also breaks. For analysis purpose, the stream can be considered as a sequence of audio and video frames with no markers of the start and end points of the included programs or breaks. Most of TV channels that produce TV streams provide a program guide about the broadcasted programs. However, such guides usually lack precision, especially with the existence of live programs which makes the prediction of their start and end very hard. Moreover, program guides do not include any information about the breaks (i.e. commercials). Hence, one of the important steps to structure TV video content is to segment it into different programs and then choose the appropriate method to segment each program separately based on its type. The TV stream structuring consists in detecting the start and end of all the programs and breaks in the stream and later trying to annotate automatically each program by some metadata that summarizes its content or identifies its type. This step can be performed by analyzing the metadata provided with the stream (EPG or EIT), or analyzing the audiovisual stream itself. In this article, we define what we call TvToC (TV stream table of content) that adds a new leve...

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Section: Program-based Methodsmentioning

confidence: 99%

TV Stream Table of Content: A New Level in the Hierarchical Video Representation

Ibrahim¹

2018

JCSA

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“…The extraction of the dominant color we applied is inspired from the work of [9]. The main difference is that our method considers that the dominant color is spread on a margin of colors in the RGB or HSV space unlike the method used in [9] where the extracted dominant color is a unique triplet of (R,G,B) or (H,S,V) values.…”

Section: Dominant Colormentioning

confidence: 99%

Face-and-clothing based people clustering in video content

Khoury

Sènac

Joly

2010

Proceedings of the International Conference on Multimedia Information Retrieval

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Content-based people clustering is a crucial step for people indexing within video documents. In this paper, we investigate the use of both face and clothing features. A method of extracting a keyface for each video sequence is proposed. An algorithm based on the average of the N -minimum pair distances between local invariant features is used in order to resolve the problem of face matching. An original method for clothing matching is proposed based on 3D histogram of the dominant color. A 3-levels hierarchical bottom-up clustering that combines local invariant features, skin color, 3D histogram and clothing texture is also described. Experiments and results show the efficiency of the proposed clustering system.

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Similarity between audiovisual contents

Joly

2011

2011 10th International Workshop on Electronics, Control, Measurement and Signals

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Mining for video production invariants to measure style similarity

Cited by 4 publications

References 6 publications

TV Stream Table of Content: A New Level in the Hierarchical Video Representation

TV Stream Table of Content: A New Level in the Hierarchical Video Representation

Face-and-clothing based people clustering in video content

Similarity between audiovisual contents

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