The document presents the sketch-finder approach for sketch-based image retrieval, highlighting its advantages over traditional methods like edgel-index. It emphasizes the ability to retrieve images with similar shapes, scales, and rotations without prior image examples. Results from evaluations indicate improved efficiency in data indexing and retrieval speed compared to existing techniques.

1. 1
Sketch-Finder – A New
Approach for Sketch-Based
Image Retrieval
Carlos Alberto F. Pimentel Filho
fragapimentel@gmail.com
Arnaldo de Albuquerque Araújo (UFMG)
Michel Crucianu (CNAM)

2. 2
Introduction
Content-Based Image Retrieval (CBIR)
Sketch-Based Image Retrieval (SBIR)
Mind-Finder Approach
Sketch-Finder Approach
Experiments
Conclusion
Future Work

3. 3
Content-Based Image Retrieval
Query-by-Sketch:
Query-by-Painting:
Query-by-Example:
Query-by-Icon:
Query-by-Text:
3

4. 4
Sketch-Based Image Retrieval
SBIR fills two gaps in image retrieval
(i) Allows specification details like object position,
scale and rotation.
(ii) Allows image retrieval when there is no example
image to use.
Our goal is: to retrieve in large datasets images
visually similar to the query sketch object's shape at
similar scale, position and rotation.

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Sketch-Based Image Retrieval
Why do We Care?

6. 6
Web Image Retrieval Personal Image Retrieval
Mobile Image Retrieval Video Retrieval

7. Sketch-Based Image Retrieval
Query-by-Sketch:
osition Sensitive:
Object's shape at similar
scale, position and rotation.
Approaches:
Mind-Finder (EI)
Sketch-Finder
Compact Hash Bits
Object Sensitive:
Object's shape at any scale,
position and rotation
Approaches (BoW):
HOG
GF-HOG
FISH
SYM-FISH

8. Mind-Finder (Edgel-Index)

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Edgel-Index
* Compares matching of edgels
* Huge number of edgels for big dataset.
* Edgel:

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Sketch-Finder
Image processing flow (dataset):
Query flow:

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Contour Detection & Threshold
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Orientation and Dilation
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Wavelet Transform
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Wedgel:
Contour signature: set of wedgels

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Similarity Measure

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Why Wavelet Transform?
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Indexing Structure
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Dataset Evaluation
For evaluating we are comparing Edgel-Index [1]
with Sketch-Finder
* Paris Dataset: 6412 images [2]
* ImageNet Dataset: Subset of 535K images [3]
[1] Yang Cao et al, Edgel index for large-scale sketch-based image search.
[2] Visual Geometry Group - http://www.robots.ox.ac.uk/~vgg/data/parisbuildings/index.html
[3] ImageNet: http://www.image-net.org/

18. Genertic Algorithm

19. Genertic Algorithm
Population is a set of 100
Selection of the best results (mAP20)
Crossover
Mutation

20. Some user sketches
Also, the Paris dataset was used to compare the e↵ec-
tiveness of our approach with the sketch-finder [9] and the
mind-finder [3]. This e ciency was evaluated considering
the precision of z best rank position, and in this paper we
used the 20 best positions as in [17].
Figure 7: Examples of the Paris sketch dataset.
To evaluate the e ciency of [9], [3] and our approach, we

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Some Results (Paris Dataset)
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ults.
rank
must
best
gen-
pa-
ex-
and
,000
ness
para-
110
s re-
rs a,

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Effectiveness
Precision vs. Recall
We used the VGG ground-truth for the Paris dataset
and built one for the ImageNet.
The same sketches were used to evaluate Mind-
Finder and Sketch-Finder

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Average - Precision vs. Recall (75)
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Efficiency Comparison - CPU

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Efficiency Comparison – I/O

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Conclusion
Sketch-Finder:
• The number of retrieved inverted files is reduced to
a small and fixed number;
• The volume of indexed data is 5% of the Edgel-
Index;
• The speed of retrieval is faster due to less amount of
data.

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Future Work
Build an android Sketch-Finder
application

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Thank You!
Carlos Alberto Fraga Pimentel Filho
fragapimentel@gmail.com