@mastersthesis {xAlfaro15, title = {Inclusion of depth information on a temporal hierarchical co-clustering technique}, year = {2015}, abstract = {

Video segmentation aims to consistently group regions that are similar in appearance and movement along a sequence. This task is an essential step of video analysis and it has important applications in video coding, indexing and retrieval, 3D reconstruction, action recognition, etc. In particular, in this thesis, a multiresolution hierarchical co-clustering technique (MRHC) is analyzed in order to include depth information for improving image segmentation in sequences with small variations. This information is invariant to changes on brightness and/or texture and camera position. Thus, it may correct some errors that are present when segmentation techniques based on color and movement are used, because this information is independent of color information of the image and the movement that occurs in the scene.

Two considerations can be made. Firstly, all the regions of the same object should share similar depth values and, secondly, depth values from regions that do not belong to the same object show discontinuities. Three different ways of coding depth information in MRHC have been studied in this project. In the first approach, the similarity between regions is weighted according to the depth difference between them. The second approach determines the 3D-neighborhood between regions. Finally, a combination of the previous approaches is considered.

The Video Occlusion/Object Boundary Dataset has been used to evaluate the inclusion of the depth on MRHC and to compare this method with the state-of-the-art techniques in the field of video segmentation. The results obtained show that the use of depth information improves the outcome video techniques obtained with the MRHC, outperforming the state-of-the-art methods in this scenario.

}, author = {Alfaro, M.}, editor = {David Varas and Marqu{\'e}s, F.} } @conference {cVaras15, title = {Multiresolution hierarchy co-clustering for semantic segmentation in sequences with small variations}, booktitle = {ICCV - International Conference on Computer Vision}, year = {2015}, month = {12/2015}, abstract = {

This paper presents a co-clustering technique that, given a collection of images and their hierarchies, clusters nodes from these hierarchies to obtain a coherent multiresolution representation of the image collection. We formalize the co-clustering as a Quadratic Semi-Assignment Problem and solve it with a linear programming relaxation approach that makes effective use of information from hierarchies. Initially, we address the problem of generating an optimal, coherent partition per image and, afterwards, we extend this method to a multiresolution framework. Finally, we particularize this framework to an iterative multiresolution video segmentation algorithm in sequences with small variations. We evaluate the algorithm on the Video Occlusion/Object Boundary Detection Dataset, showing that it produces state-of-the-art results in these scenarios.\ 

}, url = {http://arxiv.org/abs/1510.04842}, author = {David Varas and Alfaro, M. and Marqu{\'e}s, F.} } @conference {cGiro-i-Nieto11, title = {Diversity ranking for video retrieval from a broadcaster archive}, booktitle = {1st ACM International Conference on Multimedia Retrieval (ICMR {\textquoteright}11)}, year = {2011}, pages = {1{\textendash}8}, abstract = {

Video retrieval through text queries is a very common practice in broadcaster archives. The query keywords are compared to the metadata labels that documentalists have previously associated to the video assets. This paper focuses on a ranking strategy to obtain more relevant keyframes among the top hits of the results ranked lists but, at the same time, keeping a diversity of video assets. Previous solutions based on a random walk over a visual similarity graph have been modified to increase the asset diversity by filtering the edges between keyframes depending on their asset. The random walk algorithm is applied separately for ever visual feature to avoid any normalization issue between visual similarity metrics. Finally, this work evaluates performance with two separate metrics: the relevance is measured by the Average Precision and the diversity is assessed by the Average Diversity, a new metric presented in this work.

}, isbn = {978-1-4503-0336-1}, doi = {10.1145/1991996.1992052}, url = {http://dx.doi.org/10.1145/1991996.1992052}, author = {Xavier Gir{\'o}-i-Nieto and Alfaro, M. and Marqu{\'e}s, F.} } @mastersthesis {xAlfaro11 , title = {Reordenaci{\'o} i agrupament d{\textquoteright}imatges d{\textquoteright}una cerca de v{\'\i}deo}, year = {2011}, month = {01/2011}, abstract = {

La recuperaci{\'o} de v{\'\i}deo a trav{\'e}s de consultes textuals es una practica molt com{\'u} en els arxius de radiodifusi{\'o}. Les paraules clau de les consultes son comparades amb les metadades que s{\textquoteright}anoten manualment als assets de v{\'\i}deo pels documentalistes. A m{\'e}s, les cerques textuals b{\`a}siques generen llistes de resultats planes, on tots els resultats tenen la mateixa import{\`a}ncia, ja que, es limita a avaluar bin{\`a}riament si la paraula de cerca apareix o no entre les metadades associades als continguts. A m{\'e}s, acostumen a mostrar continguts molt similars, donant al usuari una llista ordenada de resultats de poca diversitat visual. La redund{\`a}ncia en els resultats provoca un malbaratament d{\textquoteright}espai a la interf{\'\i}cie gr{\`a}fica d{\textquoteright}usuari (GUI) que sovint obliga a l{\textquoteright}usuari a interactuar fortament amb la interf{\'\i}cie gr{\`a}fica fins localitzar els resultats rellevants per a la seva cerca. La aportaci{\'o} del present projecte consisteix en la presentaci{\'o} d{\textquoteright}una estrat{\`e}gia de reordenaci{\'o} i agrupaci{\'o} per obtenir keyframes de major rellev{\`a}ncia entre els primers resultats, per{\`o} al mateix temps mantenir una diversitat d{\textquoteright}assets. D{\textquoteright}aquesta forma, aquestes t{\`e}cniques permetran millorar els sistemes de visualitzaci{\'o} d{\textquoteright}imatges resultants d{\textquoteright}una cerca de v{\'\i}deo. L{\textquoteright}eina global es dissenya per ser integrada en l{\textquoteright}entorn del Digition, el gestor de continguts audiovisuals de la Corporaci{\'o} Catalana de Mitjans Audiovisuals.




}, url = {http://hdl.handle.net/2099.1/11106}, author = {Alfaro, M.}, editor = {Vives, X. and Xavier Gir{\'o}-i-Nieto} }