PR Letters: Fine-grained Categorization in Ecological Multimedia Call for Papers

Call for Papers

Special Issue on Fine-grained Categorization in Ecological Multimedia

Pattern Recognition Letters (Elsevier)

Deadline for submissions: 15th March 2015

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Description
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In the last decades, the extensive research on
recognition/classification has mainly focused on distinguishing object
classes, which are somehow dissimilar. Recently, the computer vision,
machine learning and multimedia scientific community has addressed
with increasing interest the problem of fine-grained recognition: it
refers to a subordinate level of recognition, such as recognizing
different species of animals (e.g., dogs, birds, plants) in different
types of multimedia (e.g., audio, images, videos). Of course, this
task represents a harder challenge than the “basic” object
recognition, because the discriminative features among the object
classes are more subtle and difficult to identify.

Automated systems performing such tasks might provide significant
support to many applications, especially those requiring specialized
domain knowledge (e.g. ecology): indeed, most people can easily
distinguish between a person playing a clarinet from one holding a
clarinet, while it is much more difficult to distinguish between plant
types or animal species, where inter-class similarity might be very
high. Moreover, especially for the ecological context, the need for
such automatic tools has become even greater due to technological
advances leading to a massive collection of multimedia content
(images, videos and audios) whose analysis still requires the
employment of expert human operators.

================ Topics of interest ================

- Fine-grained species recognition and classification;

- Fine-grained categorization for environment monitoring and habitat
classification;

- Transfer-learning from categories to subcategories;

- Attribute-based techniques for fine-grained categorization ;

- Ontology-based fine-grained visual categorization;

- Part-based models for fine-grained categorization/recognition;

- Fine-grained categorization with humans in the loop;

- Learning of discriminative features for fine-grained categorization;

- Integration/fusion of multi-modal data for fine-grained
categorization;

- Novel datasets for fine-grained categorization;

- Novel annotation, crowdsourcing approaches and tools for labeling
fine-grained attributes;

- Applications of computer vision and machine learning to ecological
data.


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Submission Instructions
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Submissions for this special issue must follow the standard submission
guidelines of the Pattern Recognition Letters Journal. Submissions are
made through http://ees.elsevier.com/prletters/ (special issue
acronym: FGEco). In submitting a manuscript to this special issue, the
authors acknowledge that no paper substantially similar in content has
been published or submitted for publication elsewhere.

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Important Dates
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Submission of papers: 15 March 2015
Acceptance/revision notification: 15 June 2015

Revised manuscript due: 30 August 2015

Final acceptance notification: 15 November 2015

 

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Guest Editors
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Dr. Concetto Spampinato, University of Catania, Italy, cspampin@dieei.unict.it

Dr. Vasileios Mezaris, Centre for Research and Technology Hellas, Greece, bmezaris@iti.gr

Prof. Marco Cristani, University of Verona, Italy, marco.cristani@univr.it