Thursday, April 28, 2011

CEDD descriptor for the ImageCLEF's Wikipedia Retrieval task images

Update your bookmarks: Read more about CEDD desctiptor and download an open source implementation from my personal website http://chatzichristofis.info/?page_id=15 

CEDD descriptor is available for the ImageCLEF's Wikipedia Retrieval task images. Download the compressed file from here. For each image, a .cedd file contains the cedd descriptor. In order to calculate the distance between the descriptors you can use either Euclidean or Tanimoto distance.
The visual features of the image examples are now available HERE.
This descriptor aim to aid those participants who would like to exploit the visual modality without performing feature extraction themselves. Organizers are also providing cime, tlep, and surf features.
clef

The descriptors, which include more than one features in a compact histogram, can be regarded that they belong to the family of Compact Composite Descriptors. A typical example of CCD is the CEDD descriptor. The structure of CEDD consists of 6 texture areas. In particular, each texture area is separated into 24 sub regions, with each sub region describing a color. CEDD's color information results from 2 fuzzy systems that map the colors of the image in a 24-color custom palette. To extract texture information, CEDD uses a fuzzy version of the five digital filters proposed by the MPEG-7 EHD. The CEDD extraction procedure is outlined as follows: when an image block (rectangular part of the image) interacts with the system that extracts a CCD, this section of the image simultaneously goes across 2 units. The first unit, the color unit, classifies the image block into one of the 24 shades used by the system. Let the classification be in the color $m, m \in [0,23]$. The second unit, the texture unit, classifies this section of the image in the texture area $a, a \in [0,5]$. The image block is classified in the bin $a \times 24 + m$. The process is repeated for all the image blocks of the image. On the completion of the process, the histogram is normalized within the interval [0,1] and quantized for binary representation in a three bits per bin quantization.
The most important attribute of CEDDs is the achievement of very good results that they bring up in various known benchmarking image databases.
Example:
File: 1.jpg.CEDD
101001111000000000000000001000222000000000000000101001554000000000000....
File 2.jpg.CEDD
1110000000000011014110001110000000010010001000002620000000010033137240.....
Descriptor consist of 144 integer values in the interval [0-7].
To calculate the Tanimoto distance use the following source code
4

If you use this descriptor please cite:
S. Α. Chatzichristofis and Y. S. Boutalis, “CEDD: COLOR AND EDGE DIRECTIVITY DESCRIPTOR - A COMPACT DESCRIPTOR FOR IMAGE INDEXING AND RETRIEVAL.” « 6th International Conference in advanced research on Computer Vision Systems ICVS 2008», May 12 to May 15, 2008, Santorini, Greece [Download]
OR
S. A. Chatzichristofis, K. Zagoris, Y. S. Boutalis and N. Papamarkos, “ACCURATE IMAGE RETRIEVAL BASED ON COMPACT COMPOSITE DESCRIPTORS AND RELEVANCE FEEDBACK INFORMATION”, «International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI)», Volume 24, Number 2 / February, 2010, pp. 207-244, World Scientific.
[Download the Descriptors]

5 comments:

Emanuela Boroș said...

The requested URL /downloads/cedd.rar/ was not found on this server.

Savvas Chatzichristofis said...

now the link is working again

l'apaput de la muntanya said...

Hi,
The CDD for Topics isn't
Thanks...

Savvas Chatzichristofis said...

The visual features of the image examples are now available HERE http://acsl.ee.duth.gr/Downloads/Examples.rar.

Bart said...

Hi,

the http://acsl.ee.duth.gr/Downloads/CEDD.rar link is very slow (1 byte per second), it would take me 200 days to download.