1. 1 Multiple Classifier Based on Fuzzy C-Means for a Flower Image Retrieval Keita Fukuda, Tetsuya Takiguchi, Yasuo Ariki Graduate School of Engineering, Kobe University, Japan

2. 2 Introduction –Purpose of Multiple Classifier based on Fuzzy C-means –Overview of our flower recognition system Proposed Method –Each Classifier –Fuzzy C-means Experiments Summary and Future Work Table of contents

3. 3 Introduction What is this flower ? Retrieval system requires “keywords.” But it is difficult to get “keywords” from “images.” We take a flower picture and send it to a system. We receive flower image information there and then immediately. We are focusing on flower image retrieval system In our proposed method

4. 4 Conventional techniques Conventional method Using the same features for classification. ⇒ But flowers have various shape. We propose multiple classifier which selects important features for each flower type and weights the importance on each classifier using Fuzzy c-means. It is required to select important features according to flower type.

5. 5 Overview of our system Send image Receive information Database Flower region extraction Color and shape features extraction Similarity by multiple classifier contents based flower image retrieval

6. 6 Flower region extraction A large color regions locating at near center are extracted as flower region Color and Shape features are computed on them.

7. 7 Feature extraction Color feature 0.420.030.00 10 Distribution histogram Shape feature d l d Power Freq Power spectrum Fourier transform l: contour pixel d: distance from G to contour Gravity to contour 100 segments G

8. 8 Similarity for each classifier is calculated Recognition with multiple classifier (1) Query image Image Multiple classifier 0.03 0.93 0.04 Similarity for classifier Membership of query image (Weight) Similarity Features, Information, Similarity … Database We define 3 classifiers for 3 flower types Membership of a query image in each type is obtained as weight for each similarity Linearly coupled similarity matching of 3 classifiers Which types is a query image associated with ?

9. 9 Each flower type TypeClassifierForSimilarity AFAFA “Near circle” flowersV iA BFBFB “Clear one petal” flowersV iB CFCFC “many petals” flowersV iC The similarity in each classifier is computed using Weighted Histogram Intersection. The value of weight represents the difference of each classifier We define 3 classifiers for 3 flower types: A: “ Near circle ” B: “ Clear one petal ” C: “ Many petals ”

10. 10 Each Classifier Gaussian Weight Histogram Intersection Query image image In type AIn type B Characteristics Peak (5) = the number of petal Weight of low frequency rangeWeight of band frequency range based on peak Important similarity In type C Weight of high frequency range

11. 11 Query image Image Multiple classifier 0.03 0.93 0.04 Similarity for classifier Similarity Features, Information, Similarity … Database Membership of query image (Weight) Weight for each similarity is membership of a query image in type A, B and C ⇒ It is difficult that all flowers are classified into one of 3 types clearly. Recognition with multiple classifier (2)

12. 12 Fuzzy C-means It is based on minimization of the following objective function: Fuzzy partitioning is carried out through an iterative optimization of the objective function, with the update of membership u ij and the cluster centers c j Membership property is Data elements can belong to more than one cluster. associated with each element is a set of membership.

13. 13 Fuzzy C-means For flower retrieval system 1.Database images are clustered using fuzzy c-means. 2.Membership of a query image is computed. membership { 0.88, 0.05, 0.08} Data elements { 0.43, 0.41, 0.12} {0.08, 0.12, 0.80 } C: ” Many petals ” B: ” Clear one petal ” A: ” Near Circle ” Membership of a query image is obtained as weight for each similarity {0.03, 0.93, 0.04} Input data: shape features {compactness, entropy, average} Output data: membership of the image in each type.

14. 14 Query image Image Multiple classifier 0.03 0.93 0.04 Similarity for classifier Membership (Weight) Similarity Features, Information, Similarity … Database Linearly coupled similarity matching of 3 classifiers is calculated. This example, the similarity between image “ i ” and a query image: Recognition with multiple classifier (3)

15. 15 Result information Result information are shown to users up to fifth rank based on the similarity M(i) Input image Result information

16. 16 Experimental condition Flower images of 120 species with each 4 samples. (i.e. 480 images in total). Four Cross validation (evaluate : cumulative recognition) One sample is used as a query image (120). The others are used as the database images (120×3).

17. 17 Conventional method Compactness The number of petal (peak) Moment The ratio of the shortest width over the longest Largest segment X coordinate Y coordinate Its distributed value 2 nd Largest segment X coordinate Y coordinate Its distributed value Shape features Color features y x peak

18. 18 Experimental result 1st3rd5th10th Conventional method 33.859.669.480.8 Multiple classifier No fuzzy 39.867.178.189.4 fuzzy 42.769.681.392.5 Proposed methodConventional methodquery

19. 19 New concept: multiple classifier which select important features for each flower type Summary Multiple Classifier Based on Fuzzy C-Means for a Flower Image Retrieval In future work: research for more than three classifiers

20. 20 Thank you