Category Archives: Java

Lire web demo updated – two additional global features

The LIRE web demo now includes an RGB color histogram as well as the MPEG-7 edge histogram implementation. The color histogram works well for instance for line art, such as this query.The edge histogram works fine for clear, gloabl edge distributions like queries such as this one. However, it’s performing different from PHOG. An example for the difference is this PHOG query compared to the according edge histogram query. The image below shows both queries.

PHOG-EH

Web based LIRE demo now online

A new web based LIRE demo is online. Within this demo you are able to search in an index of 300.000 images from the MIRFLICKR data set. Currently online queries from within the index are allowed, so no custom query images can be uploaded. The backend is plain LIRE, so there’s no search server and alike, and it’s the current SVN version. Search is done based on hashing, so the results are approximate, but they are immediately there. Also it’s just a selection of global features, but it’s enough to get the idea. The image below shows the result of two example searches.

LIRE web demo screen shots

 

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“Visual Information Retrieval using Java and LIRE” now available for Kindle

The Kindle version of our book “Visual Information Retrieval using Java and LIRE” is now available on amazon.com (as well as Amazon in Germany, France, Italy, and Canada). It’s a good deal with 10$ (or something like 7.90 €) for the book, which is far cheaper than the PDF version and the paperback.

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Canny Edge Detector in Java

With the implementation of the PHOG descriptor I came around the situation that no well-performing Canny Edge Detector in pure Java was available. “Pure” in my case means, that it just takes a Java BufferedImage instance and computes the edges. Therefore, I had to implement my own 🙂

As a result there is now a “simple implementation” available as part of LIRE. It takes a BufferedImage and returns another BufferedImage, which contains all the edges as black pixels, while the non-edges are white. Thresholds can be changed and the blurring filter using for preprocessing can be changed in code. Usage is dead simple:

BufferedImage in = ImageIO.read(new File("testdata/wang-1000/128.jpg")); 
CannyEdgeDetector ced = new CannyEdgeDetector(in, 40, 80); 
ImageIO.write(ced.filter(), "png", new File("out.png"));

The result is the picture below:

cannyedgeLinks

 

Large image data sets with LIRE – some new numbers

People lately asked whether LIRE can do more than linear search and I always answered: Yes, it should … but you know I never tried. But: Finally I came around to index the MIR-FLICKR data set and some of my Flickr-crawled photos and ended up with an index of 1,443,613 images. I used CEDD as main feature and a hashing algorithm to put multiple hashes per images into Lucene — to be interpreted as words. By tuning similarity, employing a Boolean query, and adding a re-rank step I ended up with a pretty decent approximate retrieval scheme, which is much faster and does not loose too many images on the way, which means the method has an acceptable recall. The image below shows the numbers along with a sample query. Linear search took more than a minute, while the hashing based approach did (nearly) the same thing in less than a second. Note that this is just a sequential, straight forward approach, so no optimization has been done to the performance. Also the hashing approach has not yet been investigated in detail, i.e. there are some parameters that still need some tuning … but let’s say it’s a step into the right direction.

Results-CEDD-Hashing

Updates on LIRE (SVN rev 39)

LIRE is not a sleeping beauty, so there’s something going on in the SVN. I recently checked in updates on Lucene (now 4.2) and Commons Math (now 3.1.1). Also I removed some deprecation things still left from Lucene 3.x.

Most notable addition however is the Extractor / Indexor class pair. They are command line applications that allow to extract global image features from images, put them into an intermediate data file and then — with the help of Indexor — write them to an index. All images are referenced relatively to the intermediate data file, so this approach can be used to preprocess a whole lot of images from different computers on a network file system. Extractor also uses a file list of images as input (one image per line) and can be therefore easily run in parallel. Just split your global file list to n smaller, non overlapping ones and run n Extractor instances. As the extraction part is the slow one, this should allow for a significant speed-up if used in parallel.

Extractor is run with

$> Extractor -i <infile> -o <outfile> -c <configfile>
  • <infile> gives the images, one per line. Use “dir /s /b *.jpg > list.txt” to create a compatible list on Windows.
  • <outfile> gives the location and name of the intermediate data file. Note: It has to be in a folder parent to all images!
  • <configfile> gives the list of features as a Java Properties file. The supported features are listed below the post. The properties file looks like:
    feature.1=net.semanticmetadata.lire.imageanalysis.CEDD
    feature.2=net.semanticmetadata.lire.imageanalysis.FCTH

Indexor is run with

Indexor -i <input-file> -l <index-directory>
  • <input-file> is the output file of Extractor, the intermediate data file.
  • <index-directory> is the directory of the index the images will be added (appended, not overwritten)

Features supported by Extractor:

  • net.semanticmetadata.lire.imageanalysis.CEDD
  • net.semanticmetadata.lire.imageanalysis.FCTH
  • net.semanticmetadata.lire.imageanalysis.OpponentHistogram
  • net.semanticmetadata.lire.imageanalysis.JointHistogram
  • net.semanticmetadata.lire.imageanalysis.AutoColorCorrelogram
  • net.semanticmetadata.lire.imageanalysis.ColorLayout
  • net.semanticmetadata.lire.imageanalysis.EdgeHistogram
  • net.semanticmetadata.lire.imageanalysis.Gabor
  • net.semanticmetadata.lire.imageanalysis.JCD
  • net.semanticmetadata.lire.imageanalysis.JpegCoefficientHistogram
  • net.semanticmetadata.lire.imageanalysis.ScalableColor
  • net.semanticmetadata.lire.imageanalysis.SimpleColorHistogram
  • net.semanticmetadata.lire.imageanalysis.Tamura

Lire 0.9.3 released

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).

Samples-JointHistogram

 

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!

News on LIRE performance

In the course of finishing the book, I reviewed several aspects of the LIRE code and came across some bugs, including one with the Jensen-Shannon divergence. This dissimilarity measure has never been used actively in any features as it didn’t work out in retrieval evaluation the way it was meant to. After two hours staring at the code the realization finally came. In Java the short if statement, “x ? y : z” is overruled by almost any operator including ‘+’. Hence,

System.out.print(true ? 1: 0 + 1) prints '1',

while

System.out.print((true ? 1: 0) + 1) prints '2'

With this problem identified I was finally able to fix the implementation of the Jensen-Shannon divergence implementation and came to new retrieval evaluation results on the SIMPLIcity data set:

map p@10 error rate
Color Histogram – JSD 0,450 0,704 0,191
Joint Histogram – JSD 0,453 0,691 0,196
Color Correlogram 0,475 0,725 0,171
Color Layout 0,439 0,610 0,309
Edge Histogram 0,333 0,500 0,401
CEDD 0,506 0,710 0,178
JCD 0,510 0,719 0,177
FCTH 0,499 0,703 0,209

Note that the color histogram in the first row now performs similarly to the “good” descriptors in terms of precision at ten and error rate. Also note that a new feature creeped in: Joint Histogram. This is a histogram combining pixel rank and RGB-64 color.

All the new stuff can be found in SVN and in the nightly builds (starting tomorrow 🙂

Lire 0.9.3_alpha – first alpha release for Lucene 4.0

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.

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