@conference {cSalvadora, title = {Cultural Event Recognition with Visual ConvNets and Temporal Models}, booktitle = {CVPR ChaLearn Looking at People Workshop 2015}, year = {2015}, month = {06/2015}, abstract = {

This paper presents our contribution to the ChaLearn Challenge 2015 on Cultural Event Classification. The challenge in this task is to automatically classify images from 50 different cultural events. Our solution is based on the combination of visual features extracted from convolutional neural networks with temporal information using a hierarchical classifier scheme. We extract visual features from the last three fully connected layers of both CaffeNet (pretrained with ImageNet) and our fine tuned version for the ChaLearn challenge. We propose a late fusion strategy that trains a separate low-level SVM on each of the extracted neural codes. The class predictions of the low-level SVMs form the input to a higher level SVM, which gives the final event scores. We achieve our best result by adding a temporal refinement step into our classification scheme, which is applied directly to the output of each low-level SVM. Our approach penalizes high classification scores based on visual features when their time stamp does not match well an event-specific temporal distribution learned from the training and validation data. Our system achieved the second best result in the \ ChaLearn Challenge 2015 on Cultural Event Classification with a mean average precision of 0.767 on the test set.

[Preprint in arXiv][Workshop site][Slides on GDrive]

Cultural Event Recognition with Visual ConvNets and Temporal Models from Xavier Giro
}, url = {http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W09/papers/Salvador_Cultural_Event_Recognition_2015_CVPR_paper.pdf}, author = {Amaia Salvador and Zeppelzauer, Matthias and Manchon-Vizuete, Daniel and Calafell, Andrea and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xManchon-Vizuete, title = {Low computational cost algorithms for photo clustering and mail signature detection in the cloud}, year = {2014}, abstract = {

Advisors: Xavier Gir{\'o}-i-Nieto (UPC) and Omar Pera (Pixable)

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

Low computational cost algorithms for photo clustering and mail signature detection in the cloud from Xavi Gir{\'o}

This Final degree thesis summarizes the tasks that have been developed during an internship in Pixable Inc. in New York City together with the tasks related to the Me- diaeval 2013 evaluation campaign, where I participated with the team of Universitat Politecnica de Catalunya (UPC). The main focus of my work was on the Photofeed service, that is a photo archive service in the cloud.

The popularisation of the storage of photos on the cloud has opened new oppor- tunities and challenges for the organization and extension of photo collections. In my thesis I have developed a light computational solution for the clustering of web photos based on social events. The proposal combines a first oversegmentation of the photo collections of each user based on temporal cues, as previously proposed in the PhotoTOC algorithm [Platt et al, PACRIM 2003]. On a second stage, the resulting mini-clusters are merged based on contextual metadata such as geolocation, keywords and user IDs.

Closely relate to photo clustering we can study mail classification too. Additional tasks were developed for the Contactive company in this field. In order to solve the problems that Contactive was facing in mail analysis tasks, I developed methods for automatically identifying signature blocks and reply lines in plain-text email messages. This analysis has many potential applications, such as preprocessing email for text-to- speech systems; anonymization of email corpora; improving automatic content-based mail classifiers; and email threading. This method is based on applying machine learning methods to a sequential representation of an email message, in which each email is represented as a sequence of lines, and each line is represented as a set of features.

Final grade: A with honors (10/10)

Daniel Manchon
}, author = {Manchon-Vizuete, Daniel}, editor = {Xavier Gir{\'o}-i-Nieto and Pera, Omar} } @conference {cManchon-Vizuete, title = {Photo Clustering of Social Events by Extending PhotoTOC to a Rich Context}, booktitle = {ICMR 2014 Workshop on Social Events in Web Multimedia (SEWM)}, year = {2014}, month = {04/2014}, publisher = {ACM}, organization = {ACM}, address = {Glasgow, Scotland}, abstract = {

The popularisation of the storage of photos on the cloud has opened new opportunities and challenges for the organisation and extension of photo collections. This paper presents a light computational solution for the clustering of web photos based on social events. The proposal combines a first over-segmentation of the photo collections of each user based on temporal cues, as previously proposed in PhotoTOC. On a second stage, the resulting mini-clusters are merged based on contextual metadata such as geolocation, keywords and user IDs. Results indicate that, although temporal cues are very relevant for event clustering, robust solutions should also consider all these additional features.

[Conference website]

Photo Clustering of Social Events by Extending PhotoTOC to a Rich Context from Xavi Gir{\'o}

}, keywords = {Clustering, Event Detection, Photo Collections}, url = {http://hdl.handle.net/2117/23009}, author = {Manchon-Vizuete, Daniel and Gris-Sarabia, Irene and Xavier Gir{\'o}-i-Nieto} } @conference {cManchon-Vizuetea, title = {UPC at MediaEval 2014 Social Event Detection Task}, booktitle = {MediaEval 2014 Workshop}, year = {2014}, month = {10/2014}, publisher = {CEUR Workshop Proceedings}, organization = {CEUR Workshop Proceedings}, address = {Barcelona}, abstract = {

This document presents the contribution of the UPC team to the Social Event Detection (SED) Subtask 1 in MediaEval 2014. This contribution extends the solution tested in the previous year with a better optimization of the parameters that determine the clustering algorithm, and by introducing an additional pass that considers the merges of all pairs of mini-clusters generated during the two first passes. Our proposal also addresses the problem of incomplete metadata by generating additional textual tags based on geolocation and natural language processing techniques.

[MediaEval 2014 website]

}, url = {http://ceur-ws.org/Vol-1263/}, author = {Manchon-Vizuete, Daniel and Gris-Sarabia, Irene and Xavier Gir{\'o}-i-Nieto} } @conference {cManchon-Vizuete13, title = {UPC at MediaEval 2013 Social Event Detection Task}, booktitle = {MediaEval 2013 Workshop}, year = {2013}, month = {10/2013}, publisher = {CEUR Workshop Proceedings}, organization = {CEUR Workshop Proceedings}, address = {Barcelona, Catalonia}, abstract = {

These working notes present the contribution of the UPC team to the Social Event Detection (SED) task in MediaEval 2013. The proposal extends the previous PhotoTOC work in the domain of shared collections of photographs stored in cloud services. An initial over-segmentation of the photo collection is later refined by merging pairs of similar clusters.

Xavier Gir{\'o}-i-Nieto, "UPC at MediaEval Social Event Detection 2013" from Xavi Gir{\'o}
}, keywords = {Benchmark, Event Detection, Photo Collections}, url = {http://ceur-ws.org/Vol-1043/mediaeval2013_submission_34.pdf}, author = {Manchon-Vizuete, Daniel and Xavier Gir{\'o}-i-Nieto} }