@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 {xTella, title = {Contextless Object Recognition with Shape-enriched SIFT and Bags of Features}, year = {2014}, abstract = {
Final thesis slides (1) from Xavi Gir{\'o}

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

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

Currently, there are highly competitive results in the field of object recognition based on the aggregation of point-based features. The aggregation process, typically with an average or max-pooling of the features generates a single vector that represents the image or region that contains the object.

The aggregated point-based features typically describe the texture around the points with descriptors such as SIFT. These descriptors present limitations for wired and textureless objects. A possible solution is the addition of shape-based information. Shape descriptors have been previously used to encode shape information and thus, recognise those types of objects. But generally an alignment step is required in order to match every point from one shape to other ones. The computational cost of the similarity assessment is high.

We purpose to enrich location and texture-based features with shape-based ones. Two main architectures are explored: On the one side, to enrich the SIFT descriptors with shape information before they are aggregated. On the other side, to create the standard Bag of Words histogram and concatenate a shape histogram, classifying them as a single vector.

We evaluate the proposed techniques and the novel features on the Caltech-101 dataset.

Results show that shape features increase the final performance. Our extension of the Bag of Words with a shape-based histogram(BoW+S) results in better performance. However, for a high number of shape features, BoW+S and enriched SIFT architectures tend to converge.

Final grade: A with honors (10/10)

}, keywords = {Bag of Words, Interest Points, object candidates, Object detection, segmentation, shape coding, SIFT, textureless objects, wired objects.}, url = {http://hdl.handle.net/2099.1/22390}, author = {Tella, Marcel}, editor = {Zeppelzauer, Matthias and Xavier Gir{\'o}-i-Nieto} }