Web image size prediction for efficient focused image crawling Paper by: Katerina Andreadou, Symeon Papadopoulos and Yiannis Kompatsiaris from Centre for Research and Technology Hellas (CERTH) – Information Technologies Institute (ITI) Presented by Katerina Iliakopoulou on CBMI 2015, June 11, 2015, Prague, Czech Republic

1. Web image size prediction for efficient
focused image crawling
Katerina Andreadou, Symeon Papadopoulos and Yiannis Kompatsiaris
Centre for Research and Technology Hellas (CERTH) – Information Technologies Institute (ITI)
CBMI 2015, June 11, 2015, Prague, Czech Republic

2. Challenges in Crawling Web Images
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• Web pages contain loads of images
• A large number of HTTP requests need to be issued
to download all of them
• Yet, the majority of
small images
– are either irrelevant
– correspond to
decorative elements

3. The Problem
• Improve the performance of our focused image crawler
 crawls images related to a given set of keywords
• Typical focused crawling metrics
– Harvest rate  the number of relevant web pages discovered
– Target precision  the number of relevant crawl links
• Proposed evaluation criteria for images
– Does the alternate text contain any of the keywords?
– Does the web page title contain any of the keywords?
Very time consuming to download and evaluate the
whole HTML content and all available images
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4. Objective: Predict Web Image Size
• Predict the size of images based solely on
– the image URL and
– the HTML metadata and HTML surrounding elements
(number of DMO siblings, depth of the DOM tree, parent
text, etc.)
• Classify the images into two groups
– SMALL  width and height smaller than 200 pixels
– BIG  width and height bigger than 400 pixels
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5. Benefits of Predicting Image Size
• Substantial gains in time for the image crawler
• We used the Apache Benchmark to time random
image requests
– average download time for an image 300 msec
– average classification time for an image  10 msec
• For all images in Common Crawl (720 million)
– 10 download threads on a single core  35 weeks
• For just the big images using our method
– 10 download threads on a single core  less than 3 weeks
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6. Related Work (Focused Crawling / Image Crawling)
• Link context algorithms rely on the lexical content of
the URL within its parent page
– The shark-search algorithm (Hersovici et al., 1998)
• Graph structure algorithms take advantage of the
structure of the Web around a page
– Focused crawling: A new approach to topic-specific web
resource discovery (Chakrabarti et al., 1999)
• Semantic analysis algorithms utilize ontologies for
semantic classification
– Ontology-focused crawling (Maedche et al., 2002)
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7. Data Collection
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• We used data from the July 2014 Common Crawl set
– petabytes of data during the last 7 years
– contains raw web page data, extracted metadata and text
– lives on Amazon S3 as part of the Amazon Public Datasets
• We created a
MapReduce job to
parse all images and
videos using EMR

8. Statistics on Common Crawl Dataset
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266 TB in size containing 3.6
billion web pages:
• 78.5M unique domains
• 8% of images big
• 40% of images small
• 20% of images have no
dimension information
We choose 400 pixels as
threshold to characterize
big images.

9. Common Crawl and Big Data Analytics
• Used in combination with a Wikipedia dump to
investigate the frequency distribution of numbers
– Number frequency on the Web (van Hage, et al., 2014)
• Question whether the heavy-tailed distributions
observed in many Web crawls are inherent in the
network or a side-effect of the crawling process
– Graph structure in the Web (Meusel et al., 2014)
• Analyze the challenges of marking up content with
microdata
– Integrating product data from websites offering microdata
markup (Petrovski et al., 2014)
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10. Method Overview
We propose a supervised machine learning approach
for web image size prediction using different features:
1. The n-grams extracted from the image URL;
2. The tokens extracted from the image URL;
3. The HTML metadata and surrounding HTML
elements;
4. The combination of textual and non-textual
features (hybrid);
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11. Method I: NG
• An n-gram is a continuous sequence of n characters
from the given image URL
• Our main hypothesis:
“URLs that correspond to BIG and SMALL
images differ substantially in wording”
• BIG : large, x-large, gallery
• SMALL : logo, avatar, small, thumb, up, down
• First attempt: use the most frequent n-grams
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12. Method II: NG-TRF (term relative frequency)
1. Collect the most frequent n-grams (n={3,4,5})
for both classes (BIG and SMALL)
2. Rank the two separate lists by frequency
3. Discard n-grams below a threshold for every list
(e.g., less than 50 occurrences in 500K images)
4. For every n-gram, compute a correlation score
5. Rank again the two lists by this score
6. Pick equal number of n-grams from both lists to
create a feature vector (e.g., 500 SMALL n-grams
and 500 BIG n-grams for a 1000-vector)
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13. Method III: TOKENS-TRF
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• Same as before but with tokens
• To produce the tokens we split the image URL by all
non alphanumeric characters (W+)

14. Method IV: NG-TSRF-IDF
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• Stands for Term Squared Relative Frequency,
Inverse Document Frequency.
• If an n-gram is very frequent in both classes, we
should discard it.
• If an n-gram is not overall very frequent but it is
very class-specific, we should include it.

15. Method V: HTML metadata features
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HTML metadata features may
reveal cues about the image size.
Examples:
• Photos are more likely than
graphics to have an alt text.
• Most photos are in JPG or PNG
format.
• Most icons and graphics are in
BMP or GIF format.

16. Evaluation
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• Training: 1M images (500K small/500K big)
• Testing: 200K images (100K small/100K big)
• Random Forest classifier (Weka)
• Experimented with LibSVM and RandomTree but RF
achieved best trade-off between accuracy and training
time
• Tested with 10, 30, 100 trees
• Performance measure:

17. Results
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• Doubling the number of n-
gram features improves the
performance
• Adding more trees to the
Random Forest classifier
improves the performance
• The NG-tsrf-idf and
TOKENS-trf have the best
performance, followed closely
by NG-trf
Hybrid

18. Results: Hybrid method
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• The hybrid method takes into account both textual
and non-textual features.
• Hypothesis: the two methods will complement each
other when aggregating their outputs:
• The adv parameter allows to give an advantage to
one of the two classifiers.

19. Conclusion - Contributions
• A supervised machine learning approach for
automatically classifying Web images according to
their size.
• Assessment of textual and non-textual features.
• A statistical analysis and evaluation on a sample of
the Common Crawl set.
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20. Future Work
• Apply the n-grams and tokens approaches to the
alternate and parent text
– create two additional classifiers and combine them with
the existing ones
• Detect more fine-grained characteristics
– landscape - portrait
– photographs - graphics
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21. Thank you!
• Resources:
Slides: http://www.slideshare.net/KaterinaAndreadou1/kandreadou-
cbmi-59
Code: https://github.com/MKLab-ITI/reveal-media-
webservice/tree/year2/src/main/java/gr/iti/mklab/reveal/clustering
Common Crawl: http://commoncrawl.org/
• Get in touch:
@kandreads / kandreadou@iti.gr
@sympapadopoulos / papadop@iti.gr
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