@unpublished {xMohedanoa, title = {Fine-tuning of CNN models for Instance Search with Pseudo-Relevance Feedback}, year = {2017}, publisher = {NIPS 2017 Women in Machine Learning Workshop}, address = {Long Beach, CA, USA}, abstract = {

CNN classification models trained on millions of labeled images have been proven to encode {\textquotedblleft}general purpose{\textquotedblright} descriptors in their intermediate layers. These descriptors are useful for a diverse range of computer vision problems~\cite{1}. However, the target task of these models is substantially different to the instance search task. While classification is concerned with distinguishing between different classes, instance search is concerned with identifying concrete instances of a particular class.\ 

In this work we propose an unsupervised approach to finetune a model for similarity learning~\cite{2}. For that, we combine two different search engines: one based on off-the-shelf CNN features, and another one on the popular SIFT features. As shown in the figure below, we observe that the information of pre-trained CNN representations and SIFT is in most of the cases complementary, which allows the generation of high quality rank lists. The fusion of the two rankings is used to generate training data for a particular dataset. A pseudo-relevance feedback strategy~\cite{3} is used for sampling images from rankings, considering the top images as positive examples of a particular instance and middle-low ranked images as negative examples.

}, author = {Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @article {aGiro-i-Nieto13, title = {From Global Image Annotation to Interactive Object Segmentation}, journal = {Multimedia Tools and Applications}, volume = {70}, year = {2014}, month = {05/2014}, chapter = {475}, abstract = {

This paper presents a graphical environment for the annotation of still images that works both at the global and local scales. At the global scale, each image can be tagged with positive, negative and neutral labels referred to a semantic class from an ontology. These annotations can be used to train and evaluate an image classifier. A finer annotation at a local scale is also available for interactive segmentation of objects. This process is formulated as a selection of regions from a precomputed hierarchical partition called Binary Partition Tree. Three different semi-supervised methods have been presented and evaluated: bounding boxes, scribbles and hierarchical navigation. The implemented Java source code is published under a free software license.

}, keywords = {annotation, Hierarchical, Interaction, Multiscale, segmentation}, doi = {10.1007/s11042-013-1374-3}, author = {Xavier Gir{\'o}-i-Nieto and Martos, Manel and Mohedano, Eva and Jordi Pont-Tuset} }