@inbook {bMohedano17, title = {Object Retrieval with Deep Convolutional Features}, booktitle = {Deep Learning for Image Processing Applications}, volume = {31}, number = {Advances in Parallel Computing}, year = {2017}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam, The Netherlands}, abstract = {

Image representations extracted from convolutional neural networks (CNNs) outdo hand-crafted features in several computer vision tasks, such as visual image retrieval. This chapter recommends a simple pipeline for encoding the local activations of a convolutional layer of a pretrained CNN utilizing the well-known Bag of Words (BoW) aggregation scheme and called bag of local convolutional features (BLCF). Matching each local array of activations in a convolutional layer to a visual word results in an assignment map, which is a compact representation relating regions of an image with a visual word. We use the assignment map for fast spatial reranking, finding object localizations that are used for query expansion. We show the suitability of the BoW representation based on local CNN features for image retrieval, attaining state-of-the-art performance on the Oxford and Paris buildings benchmarks. We demonstrate that the BLCF system outperforms the latest procedures using sum pooling for a subgroup of the challenging TRECVid INS benchmark according to the mean Average Precision (mAP) metric.

}, issn = {978-1-61499-822-8 }, doi = {10.3233/978-1-61499-822-8-137}, url = {http://ebooks.iospress.nl/volumearticle/48028}, author = {Mohedano, Eva and Amaia Salvador and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Marqu{\'e}s, F.} } @conference {cMohedano, title = {Object segmentation in images using EEG signals}, booktitle = {ACM Multimedia}, year = {2014}, month = {11/2014}, address = {Orlando, Florida (USA)}, 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 an image region, specifically a block of pixels, is displayed we estimate the probability of the block containing the object of interest using a score based on EEG activity. After several such blocks are displayed, the resulting probability map is binarized and combined with the GrabCut algorithm to segment the image into object and background regions. This study shows that BCI and simple EEG analysis are useful in locating object boundaries in images.

}, keywords = {Brain-computer interfaces, Electroencephalography, GrabCut algorithm, Interactive segmentation, Object segmentation, rapid serial visual presentation}, doi = {10.1145/2647868.2654896}, url = {http://arxiv.org/abs/1408.4363}, author = {Mohedano, Eva and Healy, Graham and Kevin McGuinness and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Smeaton, Alan F.} }