All releases of Lucene Image REtrieval
Release Notes: A major change in this version is the support of Lucene 3.0.1, which has a changed API and better performance on some operating systems. 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?).
Release Notes: This is a major release. It fixes a lot of bugs and introduces several new features including a new descriptor, a simplified way to use descriptors by introducing new generic searchers and indexers, as well as a generalized interface for image descriptors. There are also several improvements in indexing and search speed. Furthermore, retrieval performance was optimized based on the Wang 1000 data set. All new features have also found their way into LireDemo.
Release Notes: This release contains three additional features: Tamura, CEDD, and a color histogram implementation. CEDD and Tamura provide additional improvements. A FastMap implementation is included for optimization of the indexing process in a later release. 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 needs significantly more time for extraction than in the last release.
Release Notes: An issue where the scalable color descriptor (color histogram) was not compliant to the MPEG-7 standard was fixed. The color only search was changed to use the color layout descriptor and a bug in the edge histogram descriptor was hunted down. The LireDemo GUI application has also been updated: A new function for creating image mosaics has been introduced and the indexing of digital photos is now faster than ever as only the EXIF thumbnails - if available - are used instead of the whole image.
Release Notes: This release brings a new descriptor, which is kind of "more advanced version of a color histogram". The so called "color correlogram" is based on the probability to find pixels of certain colors in certain neighborhoods. In general, it is a new way to retrieve photos with LIRe based on color and color distribution, which might be very interesting for applications heavily depending on color. This release also contains the LIRe Demo, a desktop GUI application showing indexing and searching images using different descriptors.
Release Notes: Since Lire already supports Color histograms (with the MPEG-7 ScalableColor descriptor), functions for searching for colors have been integrated by by adding a searcher for color only search operations, a document builder restricted to color, and a document factory for fast and efficient creation of documents describing images with one color only.
Release Notes: This release includes one major bugfix as well as a new feature. It can easily be used to identify duplicate images within an index. The bug was responsible for missing results in the result list in the special case of equal distance to the query image. The Lucene library has been updated to 2.0.0.
Release Notes: Support for weighting has been added to the image searcher module. With weighting, the importance of texture, colour, and colour distribution for image retrieval can be specified.
Release Notes: This release updates all internals to Lucene 1.9.1 and brings the new Lucene along in the libraries. The scores are now normalized and semantically equivalent to the score value of Lucene: a score of 1.0 identifies best matches, and the lower the score the worse the match. Two critical bugfixes were made: deleted documents are now skipped and there were some issues with using the same instance of searcher for multiple searches.
Release Notes: This release adds better performance for indexing and search. Depending on use case and parameters, indexing and searching speed has doubled. Tests have shown that searching an index containing 3,890 images can be done in 64ms per search. Indexing can be as fast as 308ms per image.
