@mastersthesis {xCaselles, title = {Integrating low-level motion cues in deep video saliency}, year = {2019}, abstract = {

This thesis investigates the importance of motion when predicting saliency in videos. Naturally, humans observe both dynamic and static objects. When we are focused on watching a video, we tend to keep our eyes on the objects that are moving in the scene, items that we quickly recognize, as well as to those that attract our attention. In this work, different experiments are presented to corroborate this implication. Various approaches will be shown implementing an adaptation of the SalBCE neural network by using only motion. A simple implementation is proposed for the generation of saliency maps using previously extracted static and dynamic information from the images. The DHF1K dataset has been used for the experiment{\textquoteright}s realization.

}, keywords = {Motion, Saliency, video}, author = {Caselles, Pol}, editor = {McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xColl-Pol, title = {The Importance of Time in Visual Attention Models}, year = {2018}, abstract = {

Predicting visual attention is a very active field in the computer vision community. Visual attention is a mechanism of the visual system that can select relevant areas within a scene. Models for saliency prediction are intended to automatically predict which regions are likely to be attended by a human observer. Traditionally, ground truth saliency maps are built using only the spatial position of the fixation points, being these xation points the locations where an observer fixates the gaze when viewing a scene. In this work we explore encoding the temporal information as well, and assess it in the application of prediction saliency maps with deep neural networks. It has been observed that the later fi xations in a scanpath are usually selected randomly during visualization, specially in those images with few regions of interest. Therefore, computer vision models have dificulties learning to predict them. In this work, we explore a temporal weighting over the saliency maps to better cope with this random behaviour. The newly proposed saliency representation assigns di erent weights depending on the position in the sequence of gaze fixations, giving more importance to early timesteps than later ones. We used this maps to train MLNet, a state of the art for predicting saliency maps. MLNet predictions were evaluated and compared to the results obtained when the model has been trained using traditional saliency maps. Finally, we show how the temporally weighted saliency maps brought some improvement when used to weight the visual features in an image retrieval task.

The Importance of Time in Visual Attention Models from Universitat Polit{\`e}cnica de Catalunya
}, author = {Coll-Pol, Marta}, editor = {McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xArazo, title = {The impact of visual saliency prediction in image classification}, year = {2017}, abstract = {

Advisors: Eva Mohedano, Kevin McGuinness and Xavier Giro-i-Nieto

Program: Master{\textquoteright}s degree in Telecommunications Engineering (MET)

Grade: A (9.7/10.0)

This thesis introduces an architecture to improve the accuracy of a Convolutional Neural Network trained for image classification using visual saliency predictions from the original images. In this thesis the accuracy of a Convolutional Neural Network (CNN) trained for classification has been improved using saliency maps from the original images. The network had an AlexNet architecture and was trained using 1.2 million images from the Imagenet dataset. Two methods had been explored in order to exploit the information from the visual saliency predictions. The first methodologies implemented applied the saliency maps directly to the existing layers of the CNN, which in some cases were already trained for classification and in other they were initialized with random weights. In the second methodology the information from the saliency maps was merged from a new branch, trained at the same time as the initial CNN. In order to speed up the training of the networks the experiments were implemented using images reduced to 128x128. With this sizes the proposed model achieves 12.39\% increase in Top-1 accuracy performance with respect to the original CNN, and additionally reduces the number of parameters needed compared to AlexNet. Regarding the original size images 227x227 a model that increases 1.72\% Top-1 accuracy is proposed. To accelerate the training process of the network the images have been reduced. The methodology that provides the higher improvement in accuracy will be implemented using the original size of the images. The results will be compared to those obtained from the network trained only with the original images. All the methodologies proposed are implemented in a network previously trained for classification. Additionally the most successful methodologies will be implemented in the training of a network. The results will provide information about the best way to add saliency maps to improve the accuracy.

The impact of visual saliency prediction in image classification from Xavier Giro
}, author = {Arazo, Eric}, editor = {McGuinness, Kevin and Mohedano, Eva and Xavier Gir{\'o}-i-Nieto} } @article {aMohedano, title = {Improving Object Segmentation by using EEG signals and Rapid Serial Visual Presentation}, journal = {Multimedia Tools and Applications}, year = {2015}, month = {07/2015}, abstract = {

This paper explores the potential of brain-computer interfaces in segmenting objects from images. Our approach is centered around designing an effective method for displaying the image parts to the users such that they generate measurable brain reactions. When a block of pixels is displayed, we estimate the probability of that block containing the object of interest using a score based on EEG activity. After several such blocks are displayed in rapid visual serial presentation, the resulting probability map is binarized and combined with the GrabCut algorithm to segment the image into object and background regions. This study extends our previous work that showed how BCI and simple EEG analysis are useful in locating object boundaries in images

}, issn = {1573-7721}, doi = {10.1007/s11042-015-2805-0}, url = {http://dx.doi.org/10.1007/s11042-015-2805-0}, author = {Mohedano, Eva and Healy, Graham and Kevin McGuinness and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Smeaton, Alan F.} } @conference {cVentura, title = {Improving Spatial Codification in Semantic Segmentation}, booktitle = {IEEE International Conference on Image Processing (ICIP), 2015}, year = {2015}, month = {09/2015}, publisher = {IEEE}, organization = {IEEE}, address = {Quebec City}, abstract = {

This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem. We propose to partition the image into three regions for each object to be described: Figure, Border and Ground. This partition aims at minimizing the influence of the image context on the object description and vice versa by introducing an intermediate zone around the object contour. Furthermore, we also propose a richer visual descriptor of the object by applying a Spatial Pyramid over the Figure region. Two novel Spatial Pyramid configurations are explored: Cartesian-based and crown-based Spatial Pyramids. We test these approaches with state-of-the-art techniques and show that they improve the Figure-Ground based pooling in the Pascal VOC 2011 and 2012 semantic segmentation challenges.

Improving Spatial Codification in Semantic Segmentation from Xavier Giro
}, url = {http://arxiv.org/abs/1505.07409}, author = {Ventura, C. and Xavier Gir{\'o}-i-Nieto and Ver{\'o}nica Vilaplana and Kevin McGuinness and Marqu{\'e}s, F. and O{\textquoteright}Connor, N.} } @article {xVentura, title = {Improving Spatial Codification in Semantic Segmentation (Supplementary Material)}, year = {2015}, month = {09/2015}, abstract = {

This document contains supplementary material for the paper "Improving Spatial Codification in Semantic Segmentation" submitted to ICIP 2015. First, there is a section dedicated to the results obtained by categories when ideal object candidates (ground truth masks) are used. Then, an analysis of the results using CPMC and MCG object candidates also detailed by categories. Finally, visual results for CPMC and MCG are showed.

}, author = {Ventura, C. and Xavier Gir{\'o}-i-Nieto and Ver{\'o}nica Vilaplana and Kevin McGuinness and Marqu{\'e}s, F. and Noel E. O{\textquoteright}Connor} } @conference {cMcGuinnessa, title = {Insight DCU at TRECVID 2015}, booktitle = {TRECVID 2015 Workshop}, year = {2015}, month = {11/2015}, publisher = {NIST}, organization = {NIST}, address = {Gaithersburg, MD, USA}, abstract = {

Insight-DCU participated in the instance search (INS), semantic indexing (SIN), and localization tasks (LOC) this year.

In the INS task we used deep convolutional network features trained on external data and the query data for this year to train our system. We submitted four runs, three based on convolutional network features, and one based on SIFT/BoW. F A insightdcu 1 was an automatic run using features from the last convolutional layer of a deep network with bag-of-words encoding and achieved 0.123 mAP. F A insightdcu 2 modi ed the previous run to use re-ranking based on an R-CNN model and achieved 0.111 mAP. I A insightdcu 3, our interactive run, achieved 0.269 mAP. Our SIFT-based run F A insightdcu 2 used weak geometric consistency to improve performance over the previous year to 0.187 mAP. Overall we found that using features from the convolutional layers improved performance over features from the fully connected layers used in previous years, and that weak geometric consistency improves performance for local feature ranking.

In the SIN task we again used convolutional network features, this time netuning a network pretrained on external data for the task. We submitted four runs, 2C D A insightdcu.15 1..4 varying the top-level learning algorithm and use of concept co-occurance. 2C D A insightdcu.15 1 used a linear SVM top-level learner, and achieved 0.63 mAP. Exploiting concept co-occurance improved the accuracy of our logistic regression run 2C D A insightdcu.15 3 from 0.058 mAP to 0.6 2C D A insightdcu.15 3.

Our LOC system used training data from IACC.1.B and features similar to our INS run, but using a VLAD encoding instead of a bag-of-words. Unfortunately there was problem with the run that we are still investigating.

Note: UPC and NII participated only in the INS task of this submission.

}, url = {http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.15.org.html}, author = {Kevin McGuinness and Mohedano, Eva and Amaia Salvador and Zhang, ZhenXing and Marsden, Mark and Wang, Peng and Jargalsaikhan, Iveel and Antony, Joseph and Xavier Gir{\'o}-i-Nieto and Satoh, Shin{\textquoteright}ichi and O{\textquoteright}Connor, N. and Smeaton, Alan F.} } @conference {cMcGuinness, title = {Insight Centre for Data Analytics (DCU) at TRECVid 2014: Instance Search and Semantic Indexing Tasks}, booktitle = {2014 TRECVID Workshop}, year = {2014}, month = {11/2014}, publisher = {National Institute of Standards and Technology (NIST)}, organization = {National Institute of Standards and Technology (NIST)}, address = {Orlando, Florida (USA)}, abstract = {

Insight-DCU participated in the instance search (INS) and semantic indexing (SIN) tasks in 2014. Two very different approaches were submitted for instance search, one based on features extracted using pre-trained deep convolutional neural networks (CNNs), and another based on local SIFT features, large vocabulary visual bag-of-words aggregation, inverted index-based lookup, and geometric verification on the top-N retrieved results. Two interactive runs and two automatic runs were submitted, the best interactive runs achieved a mAP of 0.135 and the best automatic 0.12. Our semantic indexing runs were based also on using convolutional neural network features, and on Support Vector Machine classifiers with linear and RBF kernels. One run was submitted to the main task, two to the no annotation task, and one to the progress task. Data for the no-annotation task was gathered from Google Images and ImageNet. The main task run has achieved a mAP of 0.086, the best no-annotation runs had a close performance to the main run by achieving a mAP of 0.080, while the progress run had 0.043.

[2014 TREC Video Retrieval Evaluation Notebook Papers and Slides]

Poster

}, url = {http://hdl.handle.net/2117/24915}, author = {Kevin McGuinness and Mohedano, Eva and Zhang, ZhenXing and Hu, Feiyan and Albatal, Rami and Gurrin, Cathal and O{\textquoteright}Connor, N. and Smeaton, Alan F. and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Ventura, C.} } @mastersthesis {xMohedano13, title = {Investigating EEG for Saliency and Segmentation Applications in Image Processing}, year = {2013}, abstract = {

Advisors: Kevin McGuinness, Xavier Gir{\'o}-i-Nieto, Noel O{\textquoteright}Connor

School: Dublin City University (Ireland)

The main objective of this project is to implement a new way to compute saliency maps and to locate an object in an image by using a brain-computer interface. To achieve this, the project is centered in designing the proper way to display the different parts of the images to the users in such a way that they generate measurable reactions. Once an image window is shown, the objective is to compute a score based on the EEG activity and compare its result with the current automatic methods to estimate saliency maps. Also, the aim of this work is to use the EEG map as a seed for another segmentation algorithm that will extract the object from the background in an image. This study provides evidence that BCI are useful to find the location of the objects in a simple images via straightforward EEG analysis and this represents the starting point to locate objects in more complex images.

Related post on BitSearch.

Eva Mohedano, "Investigating EEG for Saliency and Segmentation Applications in Image Processing" from Xavi Gir{\'o}
}, keywords = {Brain-computer interfaces (BCI), Eelectroencephalography (EEG), rapid serial visual presentation (RSVP), saliency map, segmentation}, author = {Mohedano, Eva}, editor = {Kevin McGuinness and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} }