presentazione corso di sistemi informativi multimediali slope magnitude method
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Presentazione Corso di Sistemi Informativi Multimediali Slope Magnitude MethodTRANSCRIPT
Image Retrieval with Shape Features Extracted usingGradient Operators and Slope Magnitude Technique with
Block Truncation Coding
Dr.H.B.Kekre, Sudeep Thepade, Priyadarshini Mukherjee, Miti Kakaiya, Shobhit Wadhwa, Satyajit Singh
Docenti:Raimondo Schettini
Gianluigi Ciocca
Presentazione di Michele Pierangeli Matr. 703989
Università degli Studi di Milano BicoccaTeoria e Tecnologia della ComunicazioneCorso di Sistemi Informativi Multimedial
martedì 5 giugno 12
Introduction
The paper discusses novel image retrieval methods based on shape features extracted using gradient operators and Slope Magnitude Technique with Block Truncation Coding (BTC).
martedì 5 giugno 12
Content Based Image Retrievalkeyword search: girasole
image search:
meta tag, color -> histogram texture -> transforms or vector quantizationshape -> gradient or morphological operetorand more...
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Edge Detection MasksEdge= jump in intensity, a ramp with slope
The edge is usefull for detecting the shape of object in scene: segmentation, registration, and identification
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Edge Detection MasksSobel mask
integer value in h and v direction
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Edge Detection MasksSobel mask
integer value in h and v direction
Prewitt mask
magnitude and orentation of
edge
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Edge Detection MasksSobel mask
integer value in h and v direction
Prewitt mask Roberts mask
magnitude and orentation of
edge
h and v edge in sequence then
slope magnitude
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Edge Detection MasksSobel mask
integer value in h and v direction
Prewitt mask Roberts mask Canny mask
magnitude and orentation of
edge
h and v edge in sequence then
slope magnitude
gaussian gradient derivate,
susceptible to noise
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Slope Magnitude Method
Extract the shape features in from of connected boundaries
1 Gx mask to get gradient x2 Gy mask to get gradient y
3
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Slope Magnitude MethodSobel mask Prewitt mask Roberts mask Canny mask
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CBIR TechiniquesMask-Shape CBIRExtract feature shape with slope magnitude method on gradients of the image in H an V
Finding similarity on query with database image
Image must have the same dimension of the query
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CBIR TechiniquesMask-Shape CBIRExtract feature shape with slope magnitude method on gradients of the image in H an V
Finding similarity on query with database image
Image must have the same dimension of the query
Mask-Shape BTC CBIRExtract feature shape with slope magnitude method on gradients of the image in H an V
Resolve the problem of different image size in database
BTC applied to have a shape feature vector with consistent size
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Block Truncation CodingIt’s a lossy image compression that divides the original images into blocks and then uses a quantiser to reduce the number of grey levels in each block whilst maintaining the same mean and standard deviation.
Using sub-blocks of 4x4 pixels gives a compression ratio of 4:1.
In the coding phase if a pixel is greater or equal the block mean will have a value of 1 else 0.In the decoding phase pixel are1 repleaced by one mean pixel of the corrisponding value ( 1 or 0).
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ImplemtentationMatlab 7.0 on Interl Core 2 Duo (2.1 GHz) - 2GB RAM
Database of 1000 variable sized images witch 11 categories.
55 queries with every method
Feature similarty:errore quadratico medio
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Result and Discussion
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Result and DiscussionMask shape BTC have higer value of Precision/Recall
Robert-Shape-BTC outperforms the other methods of image retrieval.
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Result and Discussion
Gradients
Cro
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oint
of a
vara
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3 2 41
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ConclusionThe shape features can be extracted using slope magnitude method applied on gradient of images taken in both horizontal and vertical directions.
The advantage of using BTC is that there would be no need of image resizing for feature vector extraction from image with improvement in performance of image retrieval results.
In all the Robert-Mask-Shape-BTC gives the best performance.
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Fine
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