@mastersthesis {xRamos-Caballero15, title = {Keyframe-based Video Summarization Designer}, year = {2015}, abstract = {

Advisors: Xavier Gir{\'o}-i-Nieto (UPC) and Horst Eidenberger (TU Wien)

Studies: Bachelor Degree inAudiovisual Systems Engineeringat Telecom BCN-ETSETB from the Technical University of Catalonia (UPC)

Grade: B (8.7/10)

This Final Degree Work extends two previous projects and consists in carrying out an improvement of the video keyframe extraction module from one of them called Designer Master, by integrating the algorithms that were developed in the other, Object Maps.

Firstly the proposed solution is explained, which consists in a shot detection method, where the input video is sampled uniformly and afterwards, cumulative pixel-to-pixel difference is applied and a classifier decides which frames are keyframes or not.

Last, to validate our approach we conducted a user study in which both applications were compared. Users were asked to complete a survey regarding to different summaries created by means of the original application and with the one developed in this project. The results obtained were analyzed and they showed that the improvement done in the keyframes extraction module improves slightly the application performance and the quality of the generated summaries.

Keyframe-based Video Summarization Designer from Xavier Giro
}, author = {Ramos-Caballero, Carlos}, editor = {Eidenberger, Horst and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xAlmendros-Gutierrez14, title = {Visual instance mining of news videos using a graph-based approach}, year = {2014}, abstract = {

Advisors:\ Xavier Gir{\'o}-i-Nieto\ (UPC) and Horst Eidenberger (TU Wien)

Degree: Telecommunications Engineering (5 years) at\ Telecom BCN-ETSETB\ (UPC)

The aim of this thesis is to design a tool that performs visual instance\ search mining for news video summarization. This means to extract the\ relevant content of the video in order to be able to recognize the storyline\ of the news.

Initially, a sampling of the video is required to get the frames with a desired rate. Then, different relevant contents are detected from each frame, focusing on faces, text and several objects that the user can select. Next, we use a graph-based clustering method in order to recognize them with a high accuracy and select the most representative ones to show them in the visual summary. Furthermore, a graphical user interface in Wt was developed to create an online demo to test the application.

During the development of the application we have been testing the tool with the CCMA dataset. We prepared a web-based survey based on four results from this dataset to check the opinion of the users. We also validate our visual instance mining results comparing them with the results obtained applying an algorithm developed at Columbia University for video summarization. We have run the algorithm on a dataset of a few videos on two events: {\textquoteright}Boston bombings{\textquoteright} and the {\textquoteright}search of the Malaysian airlines flight{\textquoteright}. We carried out another web-based survey in which users could compare our approach with this related work. With these surveys we analyze if our tool fulfill the requirements we set up.

We can conclude that our system extract visual instances that show the most relevant content of news videos and can be used to summarize these videos effectively.

Final grade: B (7/10)

}, url = {http://hdl.handle.net/2099.1/22362}, author = {Almendros-Guti{\'e}rrez, David}, editor = {Xavier Gir{\'o}-i-Nieto and Eidenberger, Horst} } @mastersthesis {xMartos13, title = {Content-based Video Summarisation to Object Maps}, year = {2013}, abstract = {

Advisors: Xavier Gir{\'o}-i-Nieto\ and\ Horst Eidenberger

School: Vienna University of Technology (Austria)

The amount of digital video content available in the web is constantly increasing. Its handling requires efficient technologies: text search on large databases provides users a great amount of videos; the content results are accessible by a description. Users need a fast and visual way to access relevant video content effectively. Quick visualisation of content using static image summarisation is a sophisticated problem. However, it is worth it because it may solve video navigation problems. Users can very rapidly get an idea of the video with no need to browse through it with a sliding bar as normally done.

In this work a system for automatic video summarisation is developed. It creates an object map the segments of which are extracted from an input video. It allows enhancing video browsing and large video databases management generating a visual index so that the user can rapidly grasp the most relevant content. Finally, accessing them with a simple action requires several technologies that define a complex information processing.

Firstly, shot boundary detection algorithms are required to reduce time redundancy of the video. Secondly, different relevant objects are extracted from each keyframe (faces, cars, etc.). We also describe a workflow to train detection models using multiple open source solutions. Furthermore, faces are a particular and very relevant semantic class. For this reason, we use clustering methods in order to recognise them in an unsupervised recognition process. The image composition of all selected objects and faces is the final stage of the architecture. Composition is defined as the combination of distinct parts to form a whole, therefore, objects have to be rendered in the map in a visually attractive manner.

To validate our approach and assess end-user satisfaction, we conducted a user study in which we compare requirements collected by analysing related literature. We analyse redundancy and informativeness as well as pleasantness.

The results show that our approach effectively creates an image representation for videos and is able to summarise customisable content in an attractive way.


Manel Martos, "Content-based Video Summarization to Object Maps" from Image Processing Group on Vimeo.

}, url = {http://hdl.handle.net/2099.1/19359}, author = {Martos, Manel}, editor = {Xavier Gir{\'o}-i-Nieto and Eidenberger, Horst} }