I am pleased to inform you, that your article  » Improvement of natural image search engines results by emotional filtering  » is published in EUDL.

Link: http://eudl.eu/issue/ct/3/6

Improvement of natural image search engines results by emotional filtering

Abstract
With the Internet 2.0 era, managing user emotions is a problem that more and more actors are interested in. Historically, the first notions of emotion sharing were expressed and defined with emoticons. They allowed users to show their emotional status to others in an impersonal and emotionless digital world. Now, in the Internet of social media, every day users share lots of content with each other on Facebook, Twitter, Google+ and so on. Several new popular web sites like FlickR, Picassa, Pinterest, Instagram or DeviantArt are now
specifically based on sharing image content as well as personal emotional status. This kind of information is economically very valuable as it can for instance help commercial companies sell more efficiently. In fact, with this king of emotional information, business can made where companies will better target their customers needs, and/or even sell them more products. Research has been and is still interested in the mining of emotional information from user data  since then. In this paper, we focus on the impact of emotions from images that have been collected from search image engines. More  specifically our proposition is the creation of a filtering layer applied on the results of such image search engines. Our peculiarity relies in the fact that it is the first attempt from our knowledge to filter image search engines results with an emotional filtering approach.

Our paper « A CBIR based evaluation framework for visual attention models » is accepted for publication in EUSIPCO 2015.

http://www.eusipco2015.org 

abstract :

The computational models of visual attention, originally proposed as cognitive models of human attention, nowadays are being used as front-ends to numerous vision systems like automatic object recognition.  These systems are generally evaluated against eye tracking data, or manually segmented salient objects in images. We previously showed that this comparison can lead to different rankings depending on which of the two ground truths is used. These findings suggest that the saliency models ranking might be different for each application and the use of eye-tracking rankings to choose a model for a given application is not optimal. Therefore, in this paper, we propose a new saliency evaluation framework optimized for object recognition. This paper aims to answer the question: 1) Is the application-driven saliency models rankings consistent with classical ground truth like eye-tracking? 2) If not, which saliency models one should use for the precise CBIR applications?

Our paper « Multiresolution Approach Based on Adaptive Superpixels for Administrative Documents Segmentation into Color Layers » is accpeted for ICDAR 2015 http://2015.icdar.org/ 

abstract : Administrative document images are usually pro-
cessed in black and white what generates many problems due
to the errors related to the binarization. Besides all semantic
information provided by the color is lost. Document images have
a rich and highly variable content. The presence of false colors
and artefacts introduced by the scanning and the compression
alter the segmentation of the regions. Problems arise when there
is no correspondence between the point clouds which are detected
in a color space and the real regions of an image. In order to
help the segmentation, we propose the extraction of the main
colors of an image as a set of binary layers. Due to the industrial
context, our approach has to run unsupervised on a generic
dataset of color administrative documents. The originality of this
approach is the use of a multiresolution analysis to detect the
number of colors automatically. At a low resolution, a set of local
regions is obtained thanks to a SLIC-based approach which takes
into account the structure of documents and which combines
both colorimetric information and spatial information. Then, a
merging stage is applied on each resolution separately based on
the colors which have been extracted at a lower resolution. This
contribution can both feed the traditional process and exploit
colorimetric information.

AUTHORS: Elodie Carel, Jean-Christophe Burie, Vincent Courboulay, Jean-Marc Ogier and Vincent Poulain d’Andecy