HYBRID ALGORITHM USING FUZZY C-MEANS AND LOCAL BINARY PATTERNS FOR IMAGE INDEXING AND RETRIEVAL

Authors

Dilkeshwar PandeyPh. D. Student in Deptt. Mathematics

Deen Bandhu Chotu Ram University of S i & T h

Rajive KumarProfessor in Deptt. Mathematics

Deen Bandhu Chotu Ram University of S i & T hScience & Tech.

Murthal, Harayana, IndiaE-mail: dilkeshwar@hotmail.com

&

Science & Tech.Murthal, Harayana, India

E-mail: Rajiv_kansal@yahoo.com

Professor & Head in Deptt. Computer Science Engg.

ITS College of EngineeringGreater Noida UP IndiaGreater Noida, UP, India

CONTENTS:CONTENTS:Need of CBIR

Wh it U f lWhere it Useful

CBIR System

FCM Algorithm

Local Binary Pattern Operator

P d S t F kProposed System Framework

Experimental Results & Discussion

Conclusions

Need Need of CBIR?of CBIR?

Where CBIR is useful?Where CBIR is useful?

Image Retrieval SystemImage Retrieval System

Relevance Feedback

Query Formation

user Visual content Description

Feature Vector

Similarity

Visual content Description

Image Database

Feature Database

Similarity Comparison

p

Indexing & Retrieval

Retrieval resultsOutput

Fig. Content-based image retrieval system.

FCM Algorithm

Fig. : Standard FCM flowchart

LBP OperatorLBP Operator

Fi C l l i f L l Bi P (LBP)Fig. : Calculation of Local Binary Pattern (LBP) operators

Fig. : Circular neighborhood sets for different ( , )P R

Fig. : Uniform patters when P=8. The black and white dots represent the bit values of 1 and 0 in the S_LP operator

Proposed System FrameworkProposed System FrameworkAlgorithm:Input: Image; Output: Retrieval resultsInput: Image; Output: Retrieval results.

Load the input image and convert it into gray scale.P f h FCM f f lPerform the FCM of four clusters.Separate individual clusters and calculate the LBPs for eachclusters.Calculate the LBP histogram for each cluster.Form the feature vector by concatenating the four clusterhistogramshistograms.Calculate the best matches using Eq. (9).Retrieve the number of top matches.

Experimental ResultsExperimental Results( ) Number of relevant images retrievedRecall R

Total Number of relevant images=

1

1( )N

iGroup Recall GR R

N =

= ∑11 Γ

11

1( )j

Average Retrieval Rate ARR GR=

=Γ ∑

Si il i Di MSimilarity Distance Measure

2L f f, ,

11 , ,

( , )1

LgI i Q i

i I i Q i

f fD Q I

f f=

−=

+ +∑

DatabasesDatabase: DB1

COREL 1000: 10 groups (100 images per

Databases

g p ( g pgroup)

Total: 1000 images

Database: DB2

COREL 2450: 19 groups (50 to 6000 images per group)

Total: 2450 images

Category OQWC [10] GWC[8]

LBP_8_1

LBP_16_2

LBP_24_3

FLBP_8_1

FLBP_16_2

FLBP_24_3

Africans 57 7 52 9 61 8 64 4 62 4 75 3 76 5 76 8

TABLE I Results of All Techniques In terms of Precision on DB1 Database

Africans 57.7 52.9 61.8 64.4 62.4 75.3 76.5 76.8Beaches 49.3 42.0 55.4 54.3 47.4 59.8 56.5 47.0Buildings 50.9 47.8 65.4 63.3 54.5 62.7 64.3 63.1Buses 87.1 88.3 96.7 96.4 95.9 90.1 90.9 83.3Dinosaurs 74.6 96.2 98.4 96.7 95.4 100 99.8 99.5Elephants 55.7 65.9 46.3 50.7 51.4 74.7 76.6 78.6Flowers 84.3 75.5 92.2 92.5 89.5 91.6 88.1 87.4Horses 78.9 73.0 76.7 79.1 82.5 82.8 80.9 76.4Mountains 47.2 35.2 41.9 43.3 41.6 39.5 35.3 37.7Food 57 1 63 2 68 6 66 2 66 0 84 7 84 6 80 9Food 57.1 63.2 68.6 66.2 66.0 84.7 84.6 80.9Total 64.3 64.1 70.3 70.7 68.6 76.12 75.3 73.0All evaluation values are in percentage (%)

TABLE II Results of All Techniques In terms of Recall on DB1 DatabaseCategory OQWC [10] GWC[

8]LBP_8_

1LBP_16_

2LBP_24_

3FLBP_8_1 FLBP_16_2 FLBP_24_3

Africans 31.1 33.2 38.1 37.6 36.8 50.4 50.1 48.3Beaches 28.6 26.2 35.4 29.6 25.8 30.6 28.1 24.0Buildings 30.5 26.5 33.7 29.6 26.6 30.4 32.8 31.1Buildings 30.5 26.5 33.7 29.6 26.6 30.4 32.8 31.1Buses 64.0 65.1 70.5 74.2 71.6 52.7 57.8 51.5Dinosaurs 28.8 65.0 75.1 67.9 58.3 93.1 91.1 90.9Elephants 30.7 37.0 25.4 25.4 27.5 38.4 39.7 41.9Flowers 65.3 50.4 65.6 66.0 60.4 67.5 59.4 55.2H 39 9 39 5 42 2 43 4 48 8 42 6 38 1 36 70Horses 39.9 39.5 42.2 43.4 48.8 42.6 38.1 36.70Mountains 25.1 20.1 26.9 24.6 22.3 21.3 19.0 18.8Food 36.4 43.1 37.2 35.0 31.6 45.8 44.4 43.3Total 38.0 40.6 44.9 43.3 40.9 47.3 46.11 44.2All evaluation values are in percentage (%)

Fig. : Comparison of proposed method (FLBP) with other existing methods in terms: (a)–(c) average retrieval precision, (d)–(e) average retrieval rate.

Fig. . Average retrieval precision of DB2 database according to no. of topmatches considered

ConclusionsA new image indexing and retrieval algorithm is proposed in this paper by

combining FCM algorithm and local binary patterns.

Two experiments have been carried out on Corel database for proving the

worth of our algorithm.

The results after being investigated shows a significant improvement in termsThe results after being investigated shows a significant improvement in terms

of their evaluation measures as compared to LBP and other existing

transform domain techniques.

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