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Lire - Lucene Image REtrieval

The LIRE (Lucene Image REtrieval) library a simple way to create a Lucene index of image features for content based image retrieval (CBIR). This is not a complete list of features, but here are some of them:

  • ScalableColor, ColorLayout and EdgeHistogram MPEG-7
  • CEDD and FCTH (contributed by Savvas Chatzichristofis)
  • Color histograms (HSV and RGB), Tamura & Gabor, auto color correlogram, JPEG coefficient histogram (common global descriptors)
  • Visual words based on SIFT and SURF
  • Visual words based on SIMPLE
  • Approximate fast search based on hashing and metric indexing.

Furthermore, methods for searching the index based on Lucene are provided.

The LIRE library started out as part of the Caliph & Emir project and aimed to provide the CBIR features of Caliph & Emir to other Java projects in an easy and light weight way. In the meantime it has turned out as big and interesting project itself.

With Lire you can easily create an index and search through the index. LIRE 1.0 also supports local features based on bag of visual words and the SIMPLE approach, see Builders.

I recommend to start with taking a look at the SimpleAplication, either in the repository or as a release. SimpleApplication features a gradle build file and is much easier to handle. SimpleApplication is a package of LIRE, which covers the most needed stuff including indexing, search and extraction of image features for use in other applications.

If you plan to extend LIRE, it's also a good idea to work on the current Git version of LIRE: How to check out and set up LIRE in the IDEA IDE.

Note at this point that the LIRE library also comes with Apache Ant build files, named build.xml. You can use the tasks to create the jar from the source code as soon as you have Ant installed, or you are using an IDE prepared for that, like IDEA, Eclipse or NetBeans. Apache Ant can be found at the Apache Ant Project Page

If you are searching for the Solr plugin of LIRE ... there is one and it seems to be working. Several global features are working fine and it's based on Solr 6.4.0. It can be found at For questions please use the LIRE dev mailing list.

Making more of LIRE

If you need more performance out of LIRE you can consider using approximate indexing. One option is hashing, ie. BitSampling, the other is to use the approximate indexing based on metric spaces based on the work of G. Amato. Both are supported by the parallel indexer and the generic searcher class, just check the configurations and constructors.

Another option is to switch to DocValues. This utilizes another data structure of Lucene and bypasses the actual index. Please note that you need to use a custom searcher, the GenericDocValuesSearcher for searching. Indexing can be done by the parallel indexer.

How does Lire actually work?

Lire employs global image features for content based image retrieval. For more information on the underlying methods and techniques you should consult the basic literature on content based images retrieval:

Further it uses the Java search engine Lucene to provide


Parallel indexing with the ParallelIndexer running with 8 threads on a AMD A10 with 4 cores and 4.4 GHz, Windows 7 64 bits extracting 7 features at once including hashing is down to ~180 ms per image. On a Intel Core i7, ie. the 4770K, it runs a lot faster, using an SSD then speeds up the process even more. Extracting single features with the ParallelIndexer is on an Intel Core i7 typically faster than 1 MP images can be read from a (magnetic) hard disk. Example: Extracting PHOG, FCTH, CEDD and OpponentHistogram and indexing with MetricSpaces hashing with 8 concurrent threads from the MIRFlickr images stored on a Samsung SSD with a Linux Mint 17.2, OpenJDK 1.7, and a Core i7 3770K takes ~15.15 ms per image, so roughly 4 hours and 13 minutes for the whole MIRFlickr 1M data set or 1 million images.

Search is a matter of index size and number of features. Tests on CEDD with 500,000 images have shown that with cached search, LIRE needs around 870 ms per search, with DocValue based indexing and search it's around 630 ms per search.
Approximate indexing is faster with more images, for 500k images and 0.72 recall it takes around 370ms per search. This numbers are before optimization based on query bundling, multithreading. Moreover, using L1 as a distance metric can reduce search time significantly. See also here