CfP: ACM MMSys 2014 Dataset Track

The ACM Multimedia Systems conference (http://www.mmsys.org) provides a forum for researchers, engineers, and scientists to present and share their latest research findings in multimedia systems. While research about specific aspects of multimedia systems is regularly
published in the various proceedings and transactions of the networking, operating system, real-time system, and database communities, MMSys aims to cut across these domains in the context of multimedia data types. This provides a unique opportunity to view the intersections and interplay of the various approaches and solutions developed across these domains to deal with multimedia data types.

Furthermore, MMSys provides an avenue for communicating research that addresses multimedia systems holistically. As an integral part of the conference since 2012, the Dataset Track provides an opportunity for researchers and practitioners to make their work available (and citable) to the multimedia community. MMSys encourages and recognizes dataset sharing, and seeks contributions in all areas of multimedia (not limited to MM systems). Authors publishing datasets will benefit by increasing the public awareness of their effort in collecting the datasets.
In particular, authors of datasets accepted for publication will receive:

• Dataset hosting from MMSys for at least 5 years
• Citable publication of the dataset description in the proceedings published by ACM
• 15 minutes oral presentation time at the MMSys 2014 Dataset Track

All submissions will be peer-reviewed by at least two members of the technical program committee of the MMSys 2014. Datasets will be evaluated by the committee on the basis of the collection methodology and the value of the dataset as a resource for the research community.

Submission Guidelines
Authors interested in submitting a dataset should:
1. Make their data available by providing a public URL for download
2. Write a short paper describing
  a. motivation for data collection and intended use of the data set,
  b. the format of the data collected,
  c. the methodology used to collect the dataset, and
  d. basic characterizing statistics from the dataset.

Papers should be at most 6 pages long (in PDF format) prepared in the ACM style and written in English.

Submission site: http://liubei.ddns.comp.nus.edu.sg/mmsys2014-dataset/

Important dates
* Data set paper submission deadline: November 11, 2013
* Notification: December 20, 2013
* MMSys conference : March 19 - 21, 2014
** MMsys Datasets **

Previous accepted datasets can be accessed at
http://traces.cs.umass.edu/index.php/MMsys/MMsys

For further queries and extra information, please contact us at
mlux<at>itec<dot>uni-klu<dot>ac<dot>at

CBMI 2014

Following the eleven successful previous events of CBMI (Toulouse 1999, Brescia 2001, Rennes 2003, Riga 2005, Bordeaux 2007, London 2008, Chania 2009, Grenoble 2010, Madrid 2011, Annecy 2012, and Veszprem 2013), It is our pleasure to welcome you to CBMI 2014, the 12th International Content Based Multimedia Indexing Workshop , in Klagenfurt, Austria on June 18-20 2014.

The 12th International CBMI Workshop aims at bringing together the various communities involved in all aspects of content-based multimedia indexing, retrieval, browsing and presentation. The scientific program of CBMI 2014 will include invited keynote talks and regular, special and demo sessions with contributed research papers.

We sincerely hope that a carefully crafted program, the scientific discussions that the workshop will hopefully stimulate, and your additional activities in Klagenfurt and its surroundings, most importantly the lovely Lake Wörthersee, will make your CBMI 2014 participation worthwhile and a memorable experience.

Important dates:

Paper submission deadline: February 16, 2014
Notification of acceptance: March 30, 2014
Camera-ready papers due: April 14, 2014
Author registration: April 14, 2014
Early registration: May 25, 2014

http://cbmi2014.itec.aau.at/call-for-papers/

LIRE presentation at the ACM Multimedia Open Source Software Competition 2013

LIRE presentation at the ACM Multimedia Open Source Software Competition 2013 from dermotte

LIRE Solr [http://www.semanticmetadata.net/]

The Solr plugin itself is fully functional for Solr 4.4 and the source is available at https://bitbucket.org/dermotte/liresolr. There is a markdown document README.md explaining what can be done with plugin and how to actually install it. Basically it can do content based search, content based re-ranking of text searches and brings along a custom field implementation & sub linear search based on hashing.

ACM Multimedia 2013 Open Source Competition winner is….

Essentia!!! Congratulations!!!!

Essentia 2.0 beta, is an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPLv3 license (also available underproprietary license upon request). It contains an extensive collection of reusable algorithmswhich implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large set of spectral, temporal, tonal and high-level music descriptors. In addition, Essentia can be complemented with Gaia, a C++ library with python bindings which implement similarity measures and classifications on the results of audio analysis, and generate classification models that Essentia can use to compute high-level description of music (same license terms apply).

Essentia is not a framework, but rather a collection of algorithms (plus some infrastructure for multithreading and low memory usage) wrapped in a library. It doesn’t provide common high-level logic for descriptor computation (so you aren’t locked into a certain way of doing things). It rather focuses on the robustness, performance and optimality of the provided algorithms, as well as ease of use. The flow of the analysis is decided and implemented by the user, while Essentia is taking care of the implementation details of the algorithms being used. An example extractor is provided, but it should be considered as an example only, not “the” only correct way of doing things.

The library is also wrapped in Python and includes a number of predefined executable extractors for the available music descriptors, which facilitates its use for fast prototyping and allows setting up research experiments very rapidly. Furthermore, it includes a Vamp plugin to be used with Sonic Visualiser for visualization purposes. The library is cross-platform and currently supports Linux, Mac OS X, and Windows systems. Essentia is designed with a focus on the robustness of the provided music descriptors and is optimized in terms of the computational cost of the algorithms. The provided functionality, specifically the music descriptors included in-the-box and signal processing algorithms, is easily expandable and allows for both research experiments and development of large-scale industrial applications.

http://essentia.upf.edu/

2 honorable mentions are OpenSMILE and SSI

List all the open source projects presented at the ACM Multimedia conference

ACM Multimedia 2013 best paper award goes to….

ACM Multimedia 2013 best paper award goes to Attribute-augmented Semantic Hierarchy for Image Retrieval

This paper presents a novel Attribute-augmented Semantic Hierarchy (A² SH) and demonstrates its effectiveness in bridging both the semantic and intention gaps in Content-based Image Retrieval (CBIR). A² SH organizes the semantic concepts into multiple semantic levels and augments each concept with a set of related attributes, which describe the multiple facets of the concept and act as the intermediate bridge connecting the concept and low-level visual content. A hierarchical semantic similarity function is learnt to characterize the semantic similarities among images for retrieval. To better capture user search intent, a hybrid feedback mechanism is developed, which collects hybrid feedbacks on attributes and images. These feedbacks are then used to refine the search results based on A² SH. We develop a content-based image retrieval system based on the proposed A² SH. We conduct extensive experiments on a large-scale data set of over one million Web images. Experimental results show that the proposed A² SH can characterize the semantic affinities among images accurately and can shape user search intent precisely and quickly, leading to more accurate search results as compared to state-of-the-art CBIR solutions.

http://dl.acm.org/citation.cfm?id=2502093

Novaemötions dataset

This dataset contains the facial expression images captured using the novaemötions game. It contains over 40,000 images, labeled with the challenged expression and the expression recognized by the game algorithm, augmented with labels obtained through crowdsourcing.

If you are interested in obtaining the dataset, contact a.mourao@campus.fct.unl.pt