Tag Archives: Library

Apache Commons Sanselan – Image and Metadata I/O

Apache Commons has a nice sub project called Sanselan. It’s a pure Java image library for reading and writing images from and to PNG, PSD (partially), GIF, BMP, ICO, TGA, JPEG and TIFF. It also supports EXIF, IPTC and XMP metadata formats, read for all, write for some. Examples for reading and writing images, EXIF, guessing image formats etc. are provided in the source package. Currently Sanselan is available in version 0.9.7 and the release date of this version seems to be in 2009. I’m not sure if this counts as abandoned project, but it definitely doesn’t count as alive 🙂

Face Detection in Java

Face detection is basically a common tasks in image retrieval and management. However, finding a stable, well maintained and free-to-use Java library for face detection may prove hard. The OpenIMAJ project contains a common approach and yields rather fine results. However, the packaged version of all the JARs used in OpenIMAJ is quite bunch of classes making up a 30 MB jar file.

For those of you just interested in face detection I compiled and packaged the classes needed for this tasks in a ~5MB file. Finding the faces then with this library is actually a 3 lines of code task:

MBFImage image = ImageUtilities.readMBF(new FileInputStream(“image.jpg”));
FaceDetector<DetectedFace,FImage> fd = new HaarCascadeDetector(80);
List<DetectedFace> faces = fd. detectFaces (Transforms.calculateIntensity(image));

All the imports needed along with their dependencies are packaged in the facedetect-openimaj.jar file (see archive below).



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.