The ACM Multimedia Open-Source Software Competition celebrates the invaluable contribution of researchers and software developers who advance the field by providing the community with implementations of codecs, middleware, frameworks, toolkits, libraries, applications, and other multimedia software. This year will be the sixth year in running the competition as part of the ACM Multimedia program.
To qualify, software must be provided with source code and licensed in such a manner that it can be used free of charge in academic and research settings. For the competition, the software will be built from the sources. All source code, license, installation instructions and other documentation must be available on a public web page. Dependencies on non-open source third-party software are discouraged (with the exception of operating systems and commonly found commercial packages available free of charge). To encourage more diverse participation, previous years’ non-winning entries are welcome to re-submit for the 2013 competition. Student-led efforts are particularly encouraged.
Authors are highly encouraged to prepare as much documentation as possible, including examples of how the provided software might be used, download statistics or other public usage information, etc. Entries will be peer-reviewed to select entries for inclusion in the conference program as well as an overall winning entry, to be recognized formally at ACM Multimedia 2013. The criteria for judging all submissions include broad applicability and potential impact, novelty, technical depth, demo suitability, and other miscellaneous factors (e.g., maturity, popularity, student-led, no dependence on closed source, etc.).
Authors of the winning entry, and possibly additional selected entries, will be invited to demonstrate their software as part of the conference program. In addition, accepted overview papers will be included in the conference proceedings.
more information …
- Open Source Software Submission Deadline: May 13, 2013
- Notification of Acceptance: June 30, 2013
The LIRE web demo now includes an RGB color histogram as well as the MPEG-7 edge histogram implementation. The color histogram works well for instance for line art, such as this query.The edge histogram works fine for clear, gloabl edge distributions like queries such as this one. However, it’s performing different from PHOG. An example for the difference is this PHOG query compared to the according edge histogram query. The image below shows both queries.
A new web based LIRE demo is online. Within this demo you are able to search in an index of 300.000 images from the MIRFLICKR data set. Currently online queries from within the index are allowed, so no custom query images can be uploaded. The backend is plain LIRE, so there’s no search server and alike, and it’s the current SVN version. Search is done based on hashing, so the results are approximate, but they are immediately there. Also it’s just a selection of global features, but it’s enough to get the idea. The image below shows the result of two example searches.
The Kindle version of our book “Visual Information Retrieval using Java and LIRE” is now available on amazon.com (as well as Amazon in Germany, France, Italy, and Canada). It’s a good deal with 10$ (or something like 7.90 €) for the book, which is far cheaper than the PDF version and the paperback.
Luke, the tool to inspect Lucene indexes, is great and comes in handy if you work with Lucene. Unfortunately development seems to have slowed down (which is totally understandable to me, LIRE does the same all the time:)
However, the current binary is not able to open indexes for Lucene 4.2, neither is the SVN version. Still, Luboš Košco made an effort and put the source of an adapted version online. (Thanks to Luboš at this point!)
I basically just did the trick of cloning the repository and running the ant task to create the binary, but I still need to publish it for those who don’t want to compile their own version. Feel free to download it and use: Download luke-4.2.0.jar
Basic instructions: Run this jar with double click in your file manager or with “java -jar luke-4.2.0.jar” on the command line / bash. Then select the index you need and you are fine to go.
The realization that setting up the project is not too trivial led to the video howto. It’s available on YouTube and shows all steps from (an already started) fresh IntelliJ IDEA to running a Junit test for LIRE. Make sure you watch the video in 1080p / full HD to be able to read all the text.
With the implementation of the PHOG descriptor I came around the situation that no well-performing Canny Edge Detector in pure Java was available. “Pure” in my case means, that it just takes a Java BufferedImage instance and computes the edges. Therefore, I had to implement my own
As a result there is now a “simple implementation” available as part of LIRE. It takes a BufferedImage and returns another BufferedImage, which contains all the edges as black pixels, while the non-edges are white. Thresholds can be changed and the blurring filter using for preprocessing can be changed in code. Usage is dead simple:
BufferedImage in = ImageIO.read(new File("testdata/wang-1000/128.jpg"));
CannyEdgeDetector ced = new CannyEdgeDetector(in, 40, 80);
ImageIO.write(ced.filter(), "png", new File("out.png"));
The result is the picture below:
Yesterday I checked in the latest LIRE revision featuring the PHOG descriptor. I basically goes along image edge lines (using the Canny Edge Detector) and makes a fuzzy histogram of gradient directions. Furthermore it does that on different pyramid levels, meaning that the image is split up like a quad-tree and all sub-images get their histogram. All histograms of levels & sub-images are concatenated and used for retrieval. First tests on the SIMPLIcity data set have shown that the current configuration of PHOG included in LIRE outperforms the EdgeHistogram descriptor.
You can find the latest version of LIRE in the SVN & in the nightly builds.
- A. Bosch, A. Zisserman & X. Munoz. 2007. Representing shape with a spatial pyramid kernel. In Proceedings of CIVR ’07 — [DOI] [PDF]
People lately asked whether LIRE can do more than linear search and I always answered: Yes, it should … but you know I never tried. But: Finally I came around to index the MIR-FLICKR data set and some of my Flickr-crawled photos and ended up with an index of 1,443,613 images. I used CEDD as main feature and a hashing algorithm to put multiple hashes per images into Lucene — to be interpreted as words. By tuning similarity, employing a Boolean query, and adding a re-rank step I ended up with a pretty decent approximate retrieval scheme, which is much faster and does not loose too many images on the way, which means the method has an acceptable recall. The image below shows the numbers along with a sample query. Linear search took more than a minute, while the hashing based approach did (nearly) the same thing in less than a second. Note that this is just a sequential, straight forward approach, so no optimization has been done to the performance. Also the hashing approach has not yet been investigated in detail, i.e. there are some parameters that still need some tuning … but let’s say it’s a step into the right direction.
The 11th International Content Based Multimedia Indexing Workshop is to bring together the various communities involved in all aspects of content-based multimedia indexing, retrieval, browsing and presentation. Following the ten successful previous events of CBMI (Toulouse 1999, Brescia 2001, Rennes 2003, Riga 2005, Bordeaux 2007, London 2008, Chania 2009, Grenoble 2010, Madrid 2011, and Annecy 2012), the University of Pannonia, Hungary organizes the 11th Context Based Multimedia Indexing Workshop on June 17-19 2013 in the historical town of Veszprém, Hungary, near the spectacular Lake Balaton. The workshop will host invited keynote talks and regular, special and demo sessions with contributed research papers.
For more information see http://cbmi2013.mik.uni-pannon.hu/