mtp presentation final

7/26/2019 Mtp Presentation Final

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Fine-grained Bird Classification

Group members:-

Rahul Kumar(B12089)

Ajay Kumar(B12039)

Menor: Arna! Bha!sar


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Problem Statement

We are given a set of images of the different birds , where each

bird image is labelled with class name which it belongs to , and

we are going to build a model which will first detect the bird

location and then , will classify into the corresponding classes.


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"aa #e

We are training our model on Caltech-CS! Birds image dataset.

"here are total #$$ classes, with %& features of each image

'ach image have(-

%. part-location labels

#. Class belongingness labels.

). *isibility attribute for that part location


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+odel Process

ur obective is to detect bird in a given image and classify it into

respective category. So our model process can be divide into two parts

%/ Bird !etection

First we process an image into our model which detect the bird

location in given image.

#/ Bird Classification

0fter the bird detection, we classify bird image which we got from

detection into their respective classes.


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+odel Process


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Bird Classification

0ccuracy(- For #-Class(- 1).#)2

+ulticlass (- 3$.42.

sing 5-C66 (- 3#2


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Bird !etection

!etecting obects in images is a challenging tas7 owing to theirvariable appearance and the wide range of position that they can

adopt.

So, first we need a robust feature set that allows the bird form

to be discriminated cleanly, even in cluttered bac7grounds under

difficult illumination. For any bird image given ,image can be divided into two part

ne that contains bird and other as Bac7ground.

Bird detection become two class problem8Bird , Bac7ground/


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"emplate matching Based bird !etection

0 techni9ue for finding small part of an image which can

match the template image. So if ta7e bird part and use as

template we can find bird in image.

:n our dataset, we have different color birds so , we have

to ma7e a template which will not be affected by

different color.

So we use ;< feature which give shape information and

is not effected by different color.

For template matching, we use sum of absolutedifference.


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Sum of absolute !ifference 8S0!/

"he sum o$ absolue %&$$eren'es8#A"/ is a measure of thesimilarity between image bloc7s. :t is calculated by ta7ing the

absolute difference between each pi=el in the original bloc7 and

the corresponding pi=el in the bloc7 being used for comparison.

'g.

:mage bloc7 Search image 0fter calculation

# & & $ 1 # & &

# 1 & # 1 # 1 & %> #& $

3 > 4 3 4 3 > 4


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Clustering

luser analys&sor 'luser&nis the tas7 of grouping a set ofobects in such a way that obects in the same group 8called a

'luser/ are more similar 8in some sense or another/ to eachother than to those in other groups.

'=. ?-means clustering


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"raining process8"emplate matching

based Bird !etection/

a/ !ivide the dataset into train and test.

b/ 6ow ta7e an image and e=tract the image parts which contain

bird and rescale it and using histogram of oriented gradient

8;


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!etection process8"emplate matching

based Bird !etection/

a/ "a7e a test image and calculate hog feature.

b/ 6ow using S0! apply template matching between test hog image and

template 8which we got from training/.

c/ 6ow from step b/ we will get scores where minimum score represent best

matching.

b/ 6ow downscaled the image and calculate hog features and do same as

step b/ and step c/.

c/ 6ow from all scales, find minimum score and window siAe.d/ Choose that window as detection window.


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bect !etection using ;< Feature andSupport *ector +achine

#. ;< Featureand Support *ector +achine

For bird detection in scene image, we can divide the image into two

parts bird part and bac7ground part.

We treat bird !etection as # class problem. :n this we use histogram

of oriented gradient to e=tract the feature which give the shape,edges information.


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"raining process8;< and S*+/

a/ !ivide the dataset into train and test.

b/ 6ow ta7e an image and e=tract the image parts which contain bird

and bac7ground put it into bird class and bac7ground class. !o this for

all training images.

c/ 6ow we e=tract hog features from all bird and bac7ground images

and create training dataset and label vector which contain their

respective class label.

d/ "rain a linear S*+ model.


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!etection Process 8;< and S*+/

a/ "a7e test image and move a sliding window of a particular siAe on it

and e=tract siAe window feature and their position.

b/ 6ow downscaled the image and move sliding window of a particular

siAe on it. !o this till image siAe is greater than sliding window siAe.

c/ 6ow e=tract features using hog and calculate weight for each sliding

window.

d/ 6ow apply thresholding using 6+S and find detection bo= for bird in

image


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5esult

Bird !etection 0ccuracy

sing "emplate matching 8template mean/ >2

sing "emplate matching 8template cluster/ 132 sing ;< D S*+ 33.&2


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Future Wor7

We are wor7ing on the 5-C66 model for classification and

detection. Classification wor7 we have done 8pre used networ7/

with an accuracy of 3#2. 0nd now we are wor7ing on detection

part.


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"han7 Eou

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