cbir using colour and texture features
TRANSCRIPT
Presented by;
Jasmy Elizebeth Jose
S7 E C E, Roll No:27
SAINTGITS College of Engineering
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CONTENT BASED IMAGE RETRIEVAL BY USING
COLOUR AND TEXTURE FEATURES.
GUIDED BY: Er. Hanna Mathew
CONTENTS:
Introduction Why we go for CBIR ? Colour feature extraction Texture feature extraction CBIR Advantages Challenges Applications of CBIR Limitations and future scope. Conclusion References
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INTRODUCTION; Content-based image retrieval (CBIR) is the
application of computer vision/image processing to the image retrieval problem, that is, the problem of searching for digital images in large databases depending upon the contents of images known as features .
Features can be of two types; - low level
- high level. Image retrieval can be of two types - Text based - Content based The term CBIR seems to have originated in 1992, when
it was used by T. Kato. 3
CONTENT BASED IMAGE RETRIEVAL. Why CBIR????
Digital image database growing rapidly in sizeProfessional needs – Logo SearchDifficulty in locating images on the web Limitations inherent in metadata based
systems large range of possible uses for efficient
image retrieval.Possibility of missing images which use
different synonyms 4
COLOUR AS A FEATURE: first and most straight forward visual feature for
image retrieval. robust and simple to represent. colour system is independent of the underlying image
device. exhibit perceptual uniformity.
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COLOUR FEATURE EXTRACTION.
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START
RGB HSV
QUANTIFICATION
FEATURE VALUE COUNT
HSV
EUCLIDEAN DISTANCE
STOP
QUANTIFICATION
H=
S= V =
EUCLIDEAN DISTANCE
D= (Ai- Bi)²n
i =1
TEXTURE AS A FEATURE:Texture measures look for visual patterns in
images and how they are spatially defined.
Refers to properties that represents the surface or structure of an object
Features which represents texture is also called tones based on pixel intensity.
Texture is a difficult concept to represent. The identification of specific textures in an image is achieved primarily by modeling texture as a two-dimensional gray level variation.
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TEXTURE FEATURE EXTRACTION.
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START
RGB GREY SCALE
QUANTIFICATION
FEATURE VALUE COUNT
INTERNAL NORMALIZATION
STOP
QUANTIFICATION
RGB to GREY SCALE CONVERSION
Y=0.29xR+0.58xG+0.114xB Y=Grey scale value. R,G,B = red, green, blue colour. FEATURE MATRIX OF AN IMAGE
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Hi=[hi,1,hi,2,……………hi,N].
INTERNAL NORMALISATION
hij = ( hij – m j )
j
CBIR BLOCK DIAGRAM;
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DATABASE IMAGES
FEATURE EXTRACTION
QUERY FEATURES
FEATURE DATABAS
E
FEATURE SIMILARIT
Y MEASURE MATCHIN
G AND INDEXING
RETRIEVED IMAGES
QUERY IMAGE
CBIR USING COLOUR & TEXTURE FUSED FEATURES. Query is given from the user Colour and texture features are extracted
calculated and these stored in a matrix called feature matrix.
Feature vector also formed for the images present in the database
Euclidean distance is calculated between the feature vector of query image & database image
sorted the distance in ascending order and top K images are displayed on the screen. 11
ADVANTAGES OF CBIR:
Search for one specific image. General browsing to make an interactive choice. Search for a picture to go with a broad story or
search to illustrate a document. Good option for wide databases. Reduces human labour for interpretation.
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CHALLENGES:
Semantic gapThe semantic gap is the lack of coincidence between
the information that one can extract from the visual data .
Huge amount of objects to search among.
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APPLICATION AREAS
Architectural and engineering designArt collectionsCrime preventionGeographical information and remote sensing systemsIntellectual propertyMedical diagnosisMilitaryPhotograph archivesFace FindingTextiles Industry 14
LIMITATIONS:
Not having an universally acceptable algorithmic means of characterizing human vision in the context of image understanding.
building up on prior work or exploring novel directions is difficult.
Time for retrieval is more for databases with more than thousands of images.
FUTURE SCOPE: CBIR Using colour - texture – shape fused low level
features. CBIR Using high level features , multidimensional
features. 15
CONCLUSION:
CBIR based on color and texture features with that of the color and texture fused features, it is observed that CBIR based on color and texture fused features provides better results .
Other low level feature such as shape will be fused to make the image retrieval more efficient in future.
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REFERENCES:[1] Aparna G Nair,C S Gode“Enhancement of image retrieval by using
colour, texture and shape features”,International conference on Electronic systems,signal processing and computing technologiesDOI 10.1109/ICESC.2014.48
[2] Swapnalini Pattanaik,D G Bhalke.”Beginners to content based image retrieval”.IJSRET@2012
[3] S Jayaraman, S Esakirajan,T Veerakumar.”Digital Image processing” McGraw Hill Education Private Limited,2013.
[4] Jun Yue, Zhenbo Li, Lu Liu, Zetian Fu. “Content based image retrieval using color and texture fused features”, 1121-1127, 2011, Elsevier.
[5] Jagadeesh Pujari, Pushpalata S. N., “Content based image retrieval using color and shape descriptors”,2010 IEEE.
[6] www.wikipidea.org/CBIR/applications and research areas[7] http://www.research.ibm.com/topics/popups/deep/manage/ html/qbic.html 17
EXISTING COMMERCIAL CBIR SYSTEMS EXAMPLES.- QBIC-IBM
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SOURCE:http://www.research.ibm.com/topics/popups/deep/manage/html/qbic.html
QUERY SEARCH BASED ON SHAPE IN QBIC
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SOURCE: http://www.research.ibm.com/topics/popups/deep/manage/html/qbic.html
QUERY SEARCH BASED ON TEXTURE - QBIC
20http://www.research.ibm.com/topics/popups/deep/manage/html/qbic.html
INPUT IMAGE FOR RETRIEVAL – QUERY IMAGE.
21Source: Apurva N Ganar,C S Gode,Sachin M Jambhulkar,”Enhancement of Image Retrieval by using colour, texture and shape Features,2014 international conference on electronic system.ICESC.2014.48
CBIR USING COLOUR FEATURE.
22Source: Apurva N Ganar,C S Gode,Sachin M Jambhulkar,”Enhancement of Image Retrieval by using colour, texture and shape Features,2014 international conference on electronic system.ICESC.2014.48
CBIR USING TEXTURE FEATURES.
23Source: Apurva N Ganar,C S Gode,Sachin M Jambhulkar,”Enhancement of Image Retrieval by using colour, texture and shape Features,2014 international conference on electronic system.ICESC.2014.48
CBIR USING COLOUR & TEXTURE FUSED FEATURES
24Source: Apurva N Ganar,C S Gode,Sachin M Jambhulkar, ”Enhancement of Image Retrieval by using colour, texture and shape Features”,2014 international conference on electronic system.ICESC.2014.48
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