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Face Detection – convenience command line utility for Windows

Again Sometimes there is no time to compile OpenCV right here right now. Therefore I compiled a small win32 command line utility for this excact task. Just give a photo with some faces as the first parameter and you’ll get the faces centers, width and height on stdout.

Links

• facedetect.7z (compressed, 7-zip, 2.46M)
• facedetect-src.7z (compressed source, 7-zip, 1.16K) [update]

Searching with Lire in big datasets

Having received several complaints about the slowness of Lire when searching in 100k+ documents I took my time to write a small how to to explain approaches for search in big (relatively) data sets.

Lire has the ability to create indexes with lots of different features (descriptors, like RGB color histograms or CEDD). While this opens the opportunity to flexibility at search time as we can select the feature at the time we create a query, the index tends to get bigger and bigger and searcher take longer and longer.

With a data set of 121,379 images the index created with the features selected for default in Lire Demo has a size of 14,3 GB on the disk. In contrast to that an index just storing the CEDD feature along with the image identifier has a size of 29 MB.

Due to the size of the index also linear search tends to get slower. While for the index stripped down to the CEDD feature and the identifier searching takes (on a AMD Quad-Core computer with 4GB RAM and Java 1.7) roughly 0.33 seconds, searching the big index takes 7 minutes and 3 seconds.

So if you want to index and search big data sets (> 100.000 images for instance) I recommend to

• select which features you need,
• create the index with a minimum set of features, and
• eventually split the index per feature and select the index on the fly instead of the feature
• also you can load the index into RAM

For more on loading the index to RAM and the option to use local features read on in the developer wiki.

Lire and Lire Demo v 0.9 released

I just released Lire and Lire Demo in version 0.9 on sourceforge.net. Basically it’s the alpha version with additional speed and stability enhancements for bag of visual words (BoVW) indexing. While this has already been possible in earlier versions I re-furbished vocabulary creation (k-means clustering) and indexing to support up to 4 CPU cores. I also integrated a function to add documents to BoVW indexes incrementally. So a list of major changes since Lire 0.8 includes

• Major speed-up due to change and re-write of indexing strategies for local features
• Auto color correlation and color histogram features improved
• Re-ranking filter based on global features and LSA
• Parallel bag of visual words indexing and search supporting SURF and SIFT including incremental index updates (see also in the wiki)
• Added functionality to Lire Demo including support for new Lire features and a new result list view

Download and try:

• Source and binaries (or as tar.bz2)
• Lire Demo

Lire Demo 0.9 alpha 2 just released

Finally I found some time to go through Lire and fix several of the — for me — most annoying bugs. While this is still work in progress I have a preview with the demo uploaded to sf.net. New features are:

• Auto Color Correlogram and Color Histogram features improved
• Re-ranking based on different features supported
• Enhanced results view
• Much faster indexing (parallel, use -server switch for your JVM)
• Much faster search (re-write of the searhc code in Lire)
• New developer menu for faster switching of search features
• Re-ranking of results based on latent semantic analysis

You can find the updated Lire Demo along with a windows launcher here, Mac and Linux users please run it using “java -jar … ” or double click (if your windows manager supports actions like that

• Download Liredemo v0.9 alpha 2

The source is — of course — GPL and available in the SVN.

Visual Attention in Lire

While doing my preparations for my multimedia information systems lecture I finally came around to implement the visual attention model of Stentiford myself. I just check in the sources (SVN). The algorithm gives actually really nice results compared to its actual simplicity (implementationwise). You can see an example in the following figure. On the left hand side there is the original image and on the right hand side a visualization of the attention map. The light areas (especially the white ones) are deemed centers of attention. Sky and sand are so to say just random noise (there is a lot of “random” in this approach).

Links

• F. W. M. Stentiford, “An estimator for visual attention through competitive novelty with application to image compression,” Proc. Picture Coding Symposium, pp 101-104, Seoul, 24-27 April, 2001.

LIRe development mailing list

Few weeks ago I was asked if there is a mailing list dedicated to LIRe and development of applications with LIRe. That was reason enough for moe to create one. The mailing list is available at Google Groups and it’s called lire-dev. Please feel free to subscribe and ask (and of course answer and discuss) any questions regarding LIRe.

Links

• LIRe Dev Google Group

Lire 0.8 released

I just released LIRe v0.8. LIRe – Lucene Image Retrieval – is a Java library for easy content based image retrieval. Based on Lucene it doesn’t need a database and works reliable and rather fast. Major change in this version is the support of Lucene 3.0.1, which has a changed API and better performance on some OS. A critical bug was fixed in the Tamura feature implementation. It now definitely performs better Hidden in the depths of the code there is an implementation of the approximate fast indexing approach of G. Amato. It copes with the problem of linear search and provides a method for fast approximate retrieval for huge repositories (millions?). Unfortunately I haven’t tested with millions, just with tens thousands, which proves that it works, but it doesn’t show how fast.

Links

• Download Lire & Lire Demo

Lire v0.8 is on its way … just some more tests

I just checked in my latest code for LIRe and it looks like it’s nearly v0.8 release ready. Major changes include the use of Lucene 3.0.1, some bug fixes on descritors, several new test files (including one that shows how to do an LSA with image features) and of course an updated demo application. While everything needs a bit more testing as well as an documentation update, I can offer a pre-compiled demo here. All changed and added sources can be found in the SVN.

Links

• SVN URL and description
• LireDemo v 0.8 (preview, bin + src)

GDC Europe 09: Art & Production Management

First presentation in the morning I listened to was the one from Stefan Baier and Rainer Banninga, both co-founders of Streamline Studios. They talked about how art, coding and production are interconnected and what is there approach to manage all this.

They state that it is crucial to communicate interdisciplinary and to know people personally (meet them at least one time). For the art side they point out how crucial it is to stay within limits (memory, hardware, etc.) and to allow streaming of content. Another point they make is that you have to buy in the right tools or that you afford having an in-house dev team for tools you need (e.g. a race track editor for a racing game). An interesting artist team streamline studios has is the SWAT team. That’s 2-3 people just taking care of unexpectedthings in projects like cleaning up, changing details, removing glitches, etc. Rainer Banninga stated they had no crunch time for the last 18 month, so the approach looks good Surprisingly the Stefan and Rainer elobarate the need of standardized metadata meaning keyords. That’s a good sign that industry doesn’t really know about whats going on in standardization and acadmia or the other way round that standardization and academia are not working on solutions that can be applied in industry.

NetBeans Community Approves NetBeans 6.7 for Prime Time Release

The NetBeans community acceptance survey has voted for thhe last NetBeans 6.7 RC to be stable enough to be shipped. While this sounds great there is one minor details I consider critical for the significance of the survey: Only 182 people responded. (re-engineneered from 144 people being 79%). If we go with common numbers in empirical research ~5 % of the population take part in survey like these and therefore I conclude that the size of the NetBeans community is around 3.600 people.

NetBeans is actually having quite a a hard time with Eclipse pressing from open source and Idea pressing from commercial alternatives. Also the free IDEs of Microsoft’s .NET family affect the scene. However, I still think that if NetBeans manages to advance from the “I can do all” principle to a small and lean application development environment featuring a fast and intelligent editor and a WYSIWYG gui builder there is definitely a chance.

Links

• Announcement.