The current LireDemo 0.9.4 beta release features a new indexing routine, which is much faster than the old one. It’s based on the producer-consumer principle and makes — hopefully — optimal use of I/O and up to 8 cores of a system. Moreover, the new PHOG feature implementation is included and you can give it a try. Furthermore JCD, FCTH and CEDD got a more compact representation of their descriptors and use much less storage space now. Several small changes include parameter tuning on several descriptors and so on. All the changes have been documented in the CHANGES.txt file in the SVN.
I just uploaded Lire 0.9.3 to the all new Google Code page. This is the first version with full support for Lucene 4.0. Run time and memory performance are comparable to the version using Lucene 3.6. I’ve made several improvements in terms of speed and memory consumption along the way, mostly within the CEDD feature. Also I’ve added two new features:
JointHistogram – a 64 bit RGB color histogram joined with pixel rank in the 8-neighborhood, normalized with max-norm, quantized to [0,127], and JSD for a distance function
Opponent Histogram – a 64 bit histogram utilizing the opponent color space, normalized with max-norm, quantized to [0,127], and JSD for a distance function
Both features are fast in extraction (the second one naturally being faster as it does not investigate the neighborhood) and yield nice, visually very similar results in search. See also the image below showing 4 queries, each with the new features. The first one of a pair is always based on JointHistogram, the second is based on the OpponentHistogram (click ko see full size).
I also changed the Histogram interface to double as the double type is so much faster than float in 64 bit Oracle Java 7 VM. Major bug fix was in the JSD dissimilarity function. So many histograms now turned to use JSD instead of L1, depending on whether they performed better in the SIMPLIcity data set (see TestWang.java in the sources).
Final addition is the Lire-SimpleApplication, which provides two classes for indexing and search with CEDD, ready to compile with all libraries and an Ant build file. This may — hopefully — help those that still seek Java enlightenment
Finally this just leaves to say to all of you: Merry Christmas and a Happy New Year!
I just submitted my code to the SVN and created a download for Lire 0.9.3_alpha. This version features support for Lucene 4.0, which changed quite a bit in its API. I did not have the time to test the Lucene 3.6 version against the new one, so I actually don’t know which one is faster. I hope the new one, but I fear the old one
This is a pre-release for Lire for Lucene 4.0
Global features (like CEDD, FCTH, ColorLayout, AutoColorCorrelogram and alike) have been tested and considered working. Filters, like the ReRankFilter and the LSAFilter also work. The image shows a search for 10 images with ColorLayout and the results of re-ranking the result list with (i) CEDD and (ii) LSA. Visual words (local features), metric indexes and hashing have not been touched yet, beside making it compile, so I strongly recommend not to use them. However, due to a new weighting approach I assume that the visual word implementation based on Lucene 4.0 will — as soon as it is done — be much better in terms for retrieval performance.
I just uploaded version 0.9.2 of Lire and LireDemo to Google Code. Yes, Google Code! I also migrated (more or less in a under cover action some month ago) the SVN trunk to Google Code and will move on with development there. Main reasons were that ads were getting more and more aggressive over at sf.net and the interface of a Google Code project is so much cleaner and easier to handle from a project manager point of view.
Lire 0.9.2 fixes two bugs in KMeans and GenericImageSearcher. Both were critical. The KMeans fix allows now for the use of the bag of visual words approach. The GenericImageSearcher fix makes search much faster.
LireDemo 0.9.1 was released earlier today. Changes include several bug fixes, whereas the most critical was the one that prevented the indexing process from indexing a whole directory. Also the an SVN version of Lire has been compiled and added.
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
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
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.
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.
The new release contains three additional features: (i) Tamura texture features, (ii) Color and Edge Directivity Descriptor (CEDD) and (iii) a configurable color histogram implementation. While the last one was integrated for comparison only the other two provide additional improvements, especially the CEDD feature. Furthermore a FastMap implementation was included in the release for optimization of the indexing process in a later release. Also some bugs were fixed in the MPEG-7 EdgeHistogram descriptor provided in the cbir-library jar file and in color-only search. Note that due to the increased number of features the extensive document builder, which extracts all available features, needs significantly more time for extraction than in the last release.