SELECTION OF THE PROPER COMPACT
     COMPOSITE DESCRIPTOR FOR
     IMPROVING CONTENT BASED IMAGE
     RETRIEVAL           ...
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SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL

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SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL

  1. 1. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Presenter: Savvas A. Chatzichristofis Savvas Chatzichristofis, Mathias Lux and Yiannis Boutalis Department of Electrical & Computer Engineering Democritus University of  Thrace – Greece Institute of Information Technology ‐ Klagenfurt University  Klagenfurt, Austria Signal Processing, Pattern Recognition and Applications SPPRA 2009
  2. 2. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL • Compact Composite Descriptors (CCD) are global image descriptors capturing more than one feature at the same time, in a very compact representation. Natural Images Artificial Images Medical Images SpCL CEDD BTDH FCTH
  3. 3. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Overview • In this paper we propose a combination of two recently introduced CCDs (CEDD and FCTH) into a Joint Composite Descriptor (JCD). • We further present a method for auto descriptor selection. • Similar techniques were applied to select the most appropriate MPEG-7 descriptor, by extracting information from all the images of a dataset.
  4. 4. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL CEDD and FCTH Descriptors • The CEDD length is 54 bytes per image while FCTH length is 72 bytes per image. • The structure of these descriptors consists of n texture areas. In particular, each texture area is separated into 24 sub regions, with each sub region describing a color. • CEDD and FCTH use the same color information, as it results from 2 fuzzy systems that map the colors of the image in a 24-color custom palette.
  5. 5. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL CEDD and FCTH Descriptors
  6. 6. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Both Directions - 7 High Energy Vertical High - 6 Energy CEDD and FCTH Descriptors 135 Degree Horizontal 5 Diagonal High Energy 45 Degree Linear 4 Diagonal High Energy Vertical Both Directions 3 Activation Low Energy Horizontal Vertical Low 2 Activation Energy Non Horizontal Low 1 Directional Energy Linear Low Linear 0 Energy CEDD FCTH
  7. 7. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL CEDD and FCTH Descriptors WANG UCID NISTER CCD CEDD 0.25283 0.28234 0.11297 FCTH 0.27369 0.28737 0.09463 MPEG-7 DCD MPHSM 0.39460 - - DCD QHDM 0.54680 - - SCD 0.35520 0.46665 0.36365 CLD 0.40000 0.43216 0.2292 CSD 0.32460 - - EHD 0.50890 0.46061 0.3332 HTD 0.70540 - -
  8. 8. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Joint Composite Descriptor (JCD) • Based on the fact that the color information given by the 2 descriptors comes from the same fuzzy system, we can assume that joining the descriptors will result in the combining of texture areas carried by each descriptor. • JCD is made up of 7 texture areas, with each area made up of 24 sub regions that correspond to color areas.
  9. 9. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Joint Composite Descriptor (JCD) • The texture areas are as follows: ▫ JCD(0) Linear Area ▫ JCD(1) Horizontal Activation ▫ JCD(2) 45 Degrees Activation ▫ JCD(3) Vertical Activation ▫ JCD(4) 135 Degrees Activation ▫ JCD(5) Horizontal and Vertical Activation ▫ JCD(6) Non directional Activation
  10. 10. SELECTION BASED MEDICAL IMAGE INDEXING AND RETRIEVAL USING CONTENT OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE DESCRIPTOR A FUZZY COMPACT COMPOSITE RETRIEVAL Descriptor Implementation • We model the problem as follows: • CEDD and FCTH be available for an image. The indicator m symbolises the bin of the color of each descriptor. m ∈ [0, 23] • The indicators n and n’ determine the texture area for the CEDD and FCTH respectively n ∈ [0,5] n ' ∈ [0, 7]
  11. 11. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Descriptor Implementation • Each descriptor can be described in the following way: CEDD( j ) m , FCTH ( j ) m' n n CEDD( j )5 = bin(2 × 24 + 5) = bin(53) 4 The algorithm for the Joint Composite Descriptor can be analysed as follows:
  12. 12. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Descriptor Implementation
  13. 13. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Auto Descriptor Selection (ADS) • (i) The descriptor for search is chosen based on the query image. • (ii) The most appropriate descriptor is chosen at similarity assessment time, so within a single query the chosen descriptor may be different for different image pairs.
  14. 14. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Auto Descriptor Selection (ADS) • In retrieval scenarios a combination of different feature spaces within a single query is often not possible. • Experiments on the Wang data set have shown that with normalized similarities (mean of 0 and standard derivation of 1) Distribution of (a) CEDD, (b) FCTH and (c) JCD similarities / Wang 1000 image distributions are similar database. enough to be combined.
  15. 15. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Auto Descriptor Selection (ADS) • Given that the color information in all two descriptors is the same, the factor that will determine the suitability and capability of each descriptor is mainly found in the texture information. • The system that determines the most appropriate descriptor is a Mamdani fuzzy system of three inputs and one fuzzy output. The centroid method was used to defuzzify the output of the Mamdani model.
  16. 16. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Criterion 1: Maximum amount of information. • The first criterion shows which CCD contains the largest quantity of information.
  17. 17. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Criterion 2: Percentage of information in non-uniform texture areas. • The most appropriate descriptor is the one that contains the smallest percentage of non uniform image blocks.
  18. 18. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Criterion 3: The percentage of information in texture areas. • The third criterion considers the most appropriate descriptor to be the one that has the smallest percentage of image blocks present in linear areas.
  19. 19. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Experiments • The proposed methods have been implemented and are available as open source libraries under GNU - General public License (GPL) in the image retrieval system img(Rummager) the on line application img(Anaktisi) and image retrieval library LIRe.
  20. 20. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL • • CEDD • FCTH • JCD • Ranking For use of multiple different descriptors within one query, the ADS unit also needs to normalize the similarities based on their distribution. Based on experiments we used the normalization values given in paper.
  21. 21. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Experiments • To evaluate the performance of the proposed methods, the objective  measure called ANMRR is used. WANG UCID NISTER CEDD 0.25283 0.28234 0.11297 FCTH 0.27369 0.28737 0.09463 JCD 0.25606 0.26832 0.085486 ADS 0.24948 0.27952 0.09291 Based on Query descriptor ADS 0.24876 0.27722 0.09291 Based on Pair wise descriptor
  22. 22. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Conclusions • JCD and ADS methods are not suggested to improve the retrieval procedure. • The goal is to approach the best ANMRR that would result from CEDD and FCTH. • Nevertheless, the new JCD shows an increase in retrieval performance. • The methods for automatic selection of the most appropriate descriptor (ASD) for retrieval increases retrieval performance in all 3 experiments.
  23. 23. SELECTION OF THE PROPER COMPACT COMPOSITE DESCRIPTOR FOR IMPROVING CONTENT BASED IMAGE RETRIEVAL Thank You Ευχαριστώ Πολύ Download the img(Rummager) application from http://www.img-rummager.com

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