Image aesthetic evaluation aims to classify photos into high quality or low quality from the perspective of human. Many visual features have been explored under this formulation (handcrafted features), ranging from low-level image statistics, such as edge distributions and color histograms, to high-level photographic rules, such as the rule of thirds and golden ratio

2. Who am I ?
M a c h i n e L e a r n i n g E n g i n e e r
A m i t K u s h w a h a

3. Image Aesthetics
W H A T D E F I N E S A N I M A G E ?
★ Image aesthetic evaluation aims to classify photos into high quality or low quality from the perspective
of human.
★ Many visual features have been explored under this formulation (handcrafted features), ranging from
low-level image statistics, such as edge distributions and color histograms, to high-level photographic
rules, such as the rule of thirds and golden ratio

4. Beautiful Capture
“ B E A U T Y I S R E A L L Y I N T H E E Y E O F T H E B E H O L D E R ”
✦ While everyone has different tastes, there are universally accepted
norms when it comes to beauty – things which everyone pretty
much agrees are beautiful, like sunsets or sunrises over the
mountains or the ocean.
A S U B J E C T I V E C O N J E C T U R E

5. T E C H N I Q U E S O F I M A G E C A P T U R I N G
A S U B J E C T I V E C O N J E C T U R E
✦ Image aesthetics can be affected by the different usages of
lighting, contrast , and image composition
Beautiful Capture

6. The Two Images
F A C T O R S L I K E L I G H T I N G , C O N T R A S T , A N G L E D E T E R M I N E T H E A E S T H E T I C S

7. Significance of Aesthetic
Assessment
✦ With the ever increasing user generated content, biggest challenge is to
showcase high quality images to users.
✦ Zomato measures the performance of restaurant for ad sales with click
through rate (CTR) as one of the parameter. Restaurants with High Quality
Display Image have greater CTR.
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02
04
06
WRITESOMETHING

8. Photo Gallery
P H O T O S D O H A V E A S T O R Y

9. Let’s solve Image
Aesthetics with the
most disruptive
technology over the
last five years.

10. Deep Learning
S T A T E O F T H E A R T I N M A N Y F I E L D S N O W

11. Fixed Size Input
Constraint
D E E P M O D E L I N P U T L A Y E R
✦ This constraint of fixed size input in Deep Network
compromises the aesthetics of original image. input images
need to be transformed via cropping, scaling, or padding, which
often damages image composition, reduces image resolution,
or causes image distortion, thus compromising the aesthetics of
the original images.
Input Layer of Network

12. A M A Z I N G I N T H E I R O R I G I N A L A S P E C T R A T I O
Original Image

13. Transformed Image
R E S I Z I N G L O S E S T H E O R I G I N A L A E S T H E T I C S O F I M A G E
O R I G I N A L R E S I Z E D

14. Transformed Image
O R I G I N A L R E S I Z E D
R E S I Z I N G L O S E S T H E O R I G I N A L A E S T H E T I C S O F I M A G E

15. DEEP
LEARNING
Demystifying the Network
✦ The first few layers of networks are either convolution or pooling layer, followed by fully connected
layers.
✦ The fixed length input is only the constraint of fully-connected layer. Be it convolution or pooling,
both perform operations on input, with the spatial information.

16. Spatial Pyramid
Pooling
✦ Network with another pooling strategy, ‘spatial pyramid pooling’ just before the fully
connected layer, makes it capable of taking input of any size and generate fixed length
representation.

17. Max Pooling
O U T P U T I S P R O P O R T I O N A L T O I N P U T
Kernel size is fixed

18. Spatial Pyramid Pooling
Output dimension (Bin Size , Bin Size)

19. Spatial Pyramid Pooling

20. Fixed Bin Numbers
Fixed Kernel Size
SPP-Net
CNN

21. Training a SPP-net
Z O M A T O D A T A S E T U S E D
T R A I N I N G L O S S T R A I N I N G A C C U R A C Y
M A X - A C C U R A C Y - 7 7 . 2

22. NOREGIONOFINTEREST
Interesting Prediction Results
Spp-Score=0.46

23. REGIONOFINTERESTISPRESENT
Interesting Prediction Results
Spp-Score=0.94

24. It’s often neglected, but plays a major role in classification learning.
S C A L E I N V A R I A N T L E A R N I N G
Allows to explore CNN without the constraint of fixed size representation of input.
F I X E D S I Z E I N P U T
Take Aways
S P A T I A L P Y R A M I D P O O L I N G N E T

25. R E F E R E N C E S
✦ Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, “Spatial Pyramid Pooling in Deep
Convolutional Networks for Visual Recognition”
✦ Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, ImageNet Classification with Deep
Convolutional Neural Networks 2012
Composition-Preserving Deep Photo Aesthetics Assessment
✦ Long Mai, Hailin Jin, Feing Liu, Composition-Preserving Deep Photo Aesthetics
Assessment

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a m i t _ k u s h w a h a @ o u t l o o k . c o m
Thank You
F i n d m e h e r e