Multimedia Similarity Search (Tutorial) Prof. Dr. Thomas Seidl 1 , Dr. Christian Beecks 2 , Dr. Seran Uysal 2 1 LMU München, Lehrstuhl für Datenbanksysteme und Data Mining 2 RWTH Aachen, Lehrstuhl für Informatik 9 (Datenmanagement und –exploration) 06.03.2017, BTW 2017, Stuttgart
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Multimedia Similarity Search (Tutorial)
Prof. Dr. Thomas Seidl1, Dr. Christian Beecks2, Dr. Seran Uysal2
1 LMU München, Lehrstuhl für Datenbanksysteme und Data Mining2 RWTH Aachen, Lehrstuhl für Informatik 9 (Datenmanagement und –exploration)
06.03.2017, BTW 2017, Stuttgart
Multimedia Similarity Search (Tutorial)
Thomas Seidl | LMU München | Lehrstuhl für Datenbanksysteme und Data Mining
06.03.2017 | BTW 2017 | Stuttgart
What is this tutorial about?
1. Object representations
How to model and represent multimedia data?
2. Fundamental similarity models for multimedia data
How do distance-based similarity models look like?
3. Efficient query processing
How to process distance-based similarity queries efficiently?
4. Indexing
How to index spatial and high-dimensional multimedia data?
What are the principles behind metric and Ptolemaic indexing approaches?
1
Multimedia Similarity Search (Tutorial)
Thomas Seidl | LMU München | Lehrstuhl für Datenbanksysteme und Data Mining
06.03.2017 | BTW 2017 | Stuttgart
Tutorial Outline
1) Object Representation
Feature Extraction and Representation
Feature Aggregation
2) Fundamental Similarity Models
Dissimilarity Measures
Distance Functions for Feature Histograms
Distance Functions for Feature Signatures
3) Efficient Query Processing
Similarity Queries
Lower-Bounding: 2 examples
4) Indexing
Spatial Indexing
Metric and Ptolemaic Indexing
2
Multimedia Similarity Search (Tutorial)
Thomas Seidl | LMU München | Lehrstuhl für Datenbanksysteme und Data Mining
06.03.2017 | BTW 2017 | Stuttgart
Explosive Growth of Multimedia Data
• 4.5 million photos are uploaded to Flickr every day
http://advertising.yahoo.com/article/flickr.html
• 300 million images are uploaded to Facebook every day
Thomas Seidl | LMU München | Lehrstuhl für Datenbanksysteme und Data Mining
06.03.2017 | BTW 2017 | Stuttgart
Ptolemaic Lower Bound
• Let 𝕏, 𝛿 be a metric space and ℙ ⊆ 𝕏 be a finite set of pivot elements, the
Ptolemaic lower bound 𝛿ℙPto: 𝕏 × 𝕏 → ℝ w.r.t. ℙ is defined for all 𝑥, 𝑦 ∈ 𝕏 as
follows:
𝛿ℙPto 𝑥, 𝑦 = max
𝑝𝑖,𝑝𝑗∈ℙ
𝛿 𝑥, 𝑝𝑖 ⋅ 𝛿 𝑦, 𝑝𝑗 − 𝛿 𝑥, 𝑝𝑗 ⋅ 𝛿(𝑦, 𝑝𝑖)
𝛿(𝑝𝑖 , 𝑝𝑗)
• 𝛿ℙPto involves all pairs of pivot elements
• Each computation of 𝛿ℙPto entails 5 ⋅ ℙ
2distance computations
• Problem of distance caching becomes more apparent
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Multimedia Similarity Search (Tutorial)
Thomas Seidl | LMU München | Lehrstuhl für Datenbanksysteme und Data Mining
06.03.2017 | BTW 2017 | Stuttgart
Ptolemaic Lower Bound: Properties
• The examination of all pivot pairs is too inefficient
• Different pivot evaluation heuristics which follow the idea of minimizing 𝛿 𝑥, 𝑝𝑗 ⋅
𝛿 𝑦, 𝑝𝑖 in the numerator [LHS+11, HSL+13]:
Unbalanced heuristic
Examining those pivots 𝑝𝑖 , 𝑝𝑗 ∈ ℙ which are either close to 𝑥 or to 𝑦
Balanced heuristic
Examining those pivots 𝑝𝑖 , 𝑝𝑗 ∈ ℙ which are close to both 𝑥 and 𝑦
Both heuristics rely on storing the corresponding pivot permutations for each database
object in order to approximate 𝛿ℙPto efficienty
• Ptolemaic lower bound can be integrated in many metric access methods
Ptolemaic Pivot Table
Ptolemaic PM-Tree
Ptolemaic M-Index
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Multimedia Similarity Search (Tutorial)
Thomas Seidl | LMU München | Lehrstuhl für Datenbanksysteme und Data Mining
06.03.2017 | BTW 2017 | Stuttgart
Lower Bounds: Performance
• Comparison of lower bounds 𝛿ℙΔ and 𝛿ℙ
Pto with respect to the SQFD𝑘𝐺𝑎𝑢𝑠𝑠𝑖𝑎𝑛(𝜎) on an
image database comprising ~100𝑘 feature signatures of cardinality 40
100
Multimedia Similarity Search (Tutorial)
Thomas Seidl | LMU München | Lehrstuhl für Datenbanksysteme und Data Mining
06.03.2017 | BTW 2017 | Stuttgart
Lower Bounds: Performance cont’d
• Comparison of lower bounds 𝛿ℙΔ and 𝛿ℙ
Pto with respect to the SQFD𝑘𝐺𝑎𝑢𝑠𝑠𝑖𝑎𝑛(𝜎) on an
image database comprising ~100𝑘 feature signatures of cardinality 40
101
Multimedia Similarity Search (Tutorial)
Thomas Seidl | LMU München | Lehrstuhl für Datenbanksysteme und Data Mining
06.03.2017 | BTW 2017 | Stuttgart
Summary
• Depending on multimedia objects, different indexing approaches are feasible
• Spatial access methods are useful for multimedia objects, whose properties can
be expressed in a low-dimensional Euclidean space
• Metric access methods can deal with “non-dimensional” data
• Earth Mover’s Distance and Signature Quadratic Form Distance satisfy the metric
properties
• Signature Quadratic Form Distance additionally satisfies the Ptolemy inequality
102
Multimedia Similarity Search (Tutorial)
Thomas Seidl | LMU München | Lehrstuhl für Datenbanksysteme und Data Mining
06.03.2017 | BTW 2017 | Stuttgart
What was this tutorial about?
• Object representations
How to model and represent multimedia data?
• Fundamental similarity models for multimedia data
What is a distance-based similarity model?
• Efficient query processing
How to process distance-based similarity queries efficiently?
• Indexing
How to index spatial and high-dimensional multimedia data?
What are the principles behind metric and Ptolemaic indexing approaches?
103
Multimedia Similarity Search (Tutorial)
Thomas Seidl | LMU München | Lehrstuhl für Datenbanksysteme und Data Mining
06.03.2017 | BTW 2017 | Stuttgart
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06.03.2017 | BTW 2017 | Stuttgart
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Thomas Seidl | LMU München | Lehrstuhl für Datenbanksysteme und Data Mining
06.03.2017 | BTW 2017 | Stuttgart
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Thomas Seidl | LMU München | Lehrstuhl für Datenbanksysteme und Data Mining
06.03.2017 | BTW 2017 | Stuttgart
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06.03.2017 | BTW 2017 | Stuttgart
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Multimedia Similarity Search (Tutorial)
Thomas Seidl | LMU München | Lehrstuhl für Datenbanksysteme und Data Mining
06.03.2017 | BTW 2017 | Stuttgart
Thanks to my PhD students
• Dr.-Ing. M. Seran Uysal (vsl. 2016), RWTH Aachen U
• Dr.-Ing. Roland Assam (2015), G&D, Munich
• Prof. Dr.-Ing. Marwan Hassani (2015), TU Eindhoven, NL
• Dr. Ines Färber (2014), P3 group, Aachen
• Dr. Sergej Fries (2014), P3 group, Aachen
• Dr. Brigitte Boden (2014), DLR, Cologne
• Dr. Anca Zimmer (2013), Heidenhain, Traunreut
• Dr. Hardy Kremer (2013), Deloitte, Berlin
• Dr. Christian Beecks (2013), RWTH Aachen U
• Prof. Dr. Stephan Günnemann (2012), TUM, Munich
• Dr. Philipp Kranen (2011), Microsoft, Munich
• Dr. Marc Wichterich (2010), Amazon, USA
• Prof. Dr. Emmanuel Müller (2010), U Potsdam
• Dr. Ralph Krieger (2008), Avanade
• Dr. Christoph Brochhaus (2008), Bosch/Samsung
• Prof. Dr. Ira Assent (2008), U Aarhus, Denmark
• Anna Beer
• Janina Bleicher
• Julian Busch
• Daniyal Kazempour
• Yifeng Lu
• Florian Richter
• Sebastian Schmoll
Further and former members
• Prof. Dr. Hans-Peter Kriegel (i.R.)
• Prof. Dr. Christian Böhm
• Prof. Dr. Volker Tresp (Hon.)
• Prof. Dr. Peer Kröger (apl.)
• Prof. Dr. Matthias Schubert (apl.)
• Dr. Tobias Emrich (DSLab)
… plus their PhD students
… and many Bachelor and Master students!
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Multimedia Similarity Search (Tutorial)
Thomas Seidl | LMU München | Lehrstuhl für Datenbanksysteme und Data Mining