The document discusses big-data analytics for media management, focusing on information access, indexing, and evolving strategies in media and internet contexts. It highlights the challenges of subjective and objective indexing, the semantic gap in data representation, and the application of machine learning techniques like particle swarm optimization in image classification. Future research directions include exploring human and social aspects of multimedia through multimedia benchmarking initiatives.

1. 25/07/14 1
Krishna Chandramouli,
Associate Professor,
Media Engineering and Analytics Research Group,
School of Information Technology and Engineering,
VIT University
krishna.c@vit.ac.in
Big-Data Analytics for Media
Management

2. 25/07/14 2
Krishna Chandramouli,
Associate Professor,
Media Engineering and Analytics Research Group,
School of Information Technology and Engineering,
VIT University
krishna.c@vit.ac.in
Big-Data Analytics for Media
Management

3. Overview
 Media and Internet
 Information Access
 Subjective vs Objective Indexing
 The Semantic Gap
 Evolving Strategies
 Social Media Analysis
 Indexing Large-scale Repositories
 Future Research Directions
 Take Away Message
 Q & A
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4. Media and internet
25/07/14 4

5. Media and Internet
 In March 2013 that Flickr
had a total of 87 million
registered members and
more than 3.5 million new
images uploaded daily.
 There are currently almost
90 billion photos total on
Facebook. This means we
are, by far, the largest
photos site on the Internet.
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6. Information access
Textual search
Visual search
Search query formulation
25/07/14 6

7. Information Access
 Traditional ordering of images is achieved
through categorization of information into
logical structures
 Creation of albums
 Categorizing through date/time
 Clustering through location
 Image based search engines are gaining
popularity with the increase in power of
indexing schemes
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8. Information Access
25/07/14 8

9. Information Access
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10. Information Access
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11. Information Access
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12. Indexing
subjective or objective
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13. Subjective vs Objective Indexing
 How to uniquely name an image to make
them distinguishable?
 What names can be used to search images?
 How many names are needed to make the
images unique?
 Will all humans use the same names to
identify the images?
25/07/14 13

14. Subjective vs Objective Indexing
 Humans are culturally influenced
 Terms contain different meanings across
boundaries and cultures
 Therefore, any tag/word assigned to an image
will be considered subjective
 Objective signatures for images are generated
from the characteristics of the images
 The beginning of MPEG-7 standardisation
activities.
25/07/14 14

15. Subjective vs Objective Indexing
 Image characteristics exploited for objective
annotation include
 Colour
 Colour Layout Descriptor
 Colour Structure Descriptor
 Dominant Colour Descriptor
 Scalable Colour Descriptor
 Texture
 Texture Browsing Descriptor
 Edge Histogram Descriptor
 Homogenous Texture Descriptor
 Shape
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16. The semantic gap
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17. The Semantic Gap
 The semantic gap characterizes the difference
between two descriptions of an object by
different linguistic representations, for
instance languages or symbols.
 In computer science, the concept is relevant
whenever ordinary human activities,
observations, and tasks are transferred into a
computational representation
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18. The Semantic Gap
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19. The Semantic Gap
25/07/14 19

20. Evolving strategies
Image Classification
Visual Classifier
Knowledge Assisted Analysis
Image Retrieval and User Relevance Feedback
Multi-Concept Space Search and Retrieval
25/07/14 20

21. Evolving Strategies
 The problem of Image classification and
clustering has been the subject of active research
for last decade. Mainly attributed to the
exponential growth of digital content.
 The efficiency of the clustering and classification
algorithms can be attributed to the efficiency of
the machine learning approaches.
 To improve the performance of machine learning
algorithms, different optimisation techniques has
been employed such as Genetic Algorithms.
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22. Evolving Strategies
 Recent developments in applied and heuristic
optimisation techniques have been strongly influenced
and inspired by natural and biological systems.
 Algorithms developed from such observations are
 Ant Colony Optimisation (ACO) - based on the ability of
an ant colony to nd the shortest path between the food and
the source compared to an individual ant.
 Articial Immune System (AIS) - typically exploit the
immune system's characteristics of learning and memory
to solve a problem
 Particle Swarm Optimisation (PSO) - inspired by the
social behaviour of a flock of birds.
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23. Evolving Strategies
 In the study of "Semantic Gap", machine
learning algorithms are the building blocks
for bottom-up approach.
 Some of the applications of efficient machine
learning algorithms are:
 Automatic Content Annotation
 Knowledge Extraction
 Content Retrieval
 In the top-down approach, Ontology provides
partial understanding of human semantics.
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24. Visual classifier
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25. Slide: 25
Particle Swarm Optimisation
 In an effort to transform the social interaction of
different species into a computer simulation,
Kennedy and Eberhart developed an optimisation
technique named Particle Swarm Optimisation.
• In theory, the universal behaviour of individuals is
summarised in terms of Evaluate, Compare and
Imitate principles.

26. Slide: 26
Particle Swarm Optimisation
 Evaluate: The tendency to evaluate stimuli – to rate
them as positive or negative, attractive or repulsive is
perhaps the most ubiquitous behavioural characteristic
of living organisms.
 Compare: In almost every aspect of life, human tend to
compare with others
 Imitate: Humans imitation comprises taking the
perspective of the other person, not only imitating a
behaviour but also realising its purpose and executing
the behaviour when it is appropriate

27. Slide: 27
Particle Swarm Optimisation
valuessocialandcognitivegoverningparameterscc
particletheofpositiontherepresentstx
swarmtheforsolutionbestglobalrepresentstgbest
iparticleofsolutionbestpersonalrepresentstpbest
particleofvelocitytherepresentstvid
tvtxtx
txtgbestctxtpbestctvtv
id
d
i
ididid
iddidiidid
−
−
−
−
−
++=+
−+−+=+
21
21
,
)(
)(
)(
)(
)1()()1(
))()(())()(()()1(
 Equations governing the motion of particles in
PSO.

28. Slide: 28
Particle Swarm Optimisation
 Pseudo code for the algorithm
 Step 1: Random Initialization of Particles
 Step 2: Function Evaluation
 Step 3: Computation of personal best and global
best
 Step 4: Velocity update
 Step 5: Position update
 Step 6: Loop to step 2, until the stopping criteria
is reached

29. Slide: 29
Visual Classification Framework
 Self Organising Map
[X]
[X] - Input feature vector
Class 1 – Red
Untrained - Black
Winner Node selected
based on L2 norm
)]()[()()1( tmxthtmtm iciii −+=+

30. Slide: 30
Visual Classification Framework
Training of R-SOM network with PSO Algorithm

31. Slide: 31
Visual Classification Framework
Dual – Layer SOM Network

32. Slide: 32
Chaos-Particle Swarm Classifier
 The elementary principle of “Chaos” is introduced to
model the behaviour of particle motion.
 The theoretical discussion on Chaotic – PSO includes
the notion of “wind speed” and “wind direction”
modelling the biological atmosphere for position
update of the particles.

33. Slide: 33
Chaos-Particle Swarm Optimisation
 The wind speed and therefore the position update
equation are presented by:
particleofposition
particleofvelocity
atmosphereofeffectsupporting()*
atmosphereofeffectopposing()*
)1()1()()1(
()*()*)()1(
−
−
−
−
−
++++=+
++=+
id
id
su
op
w
wididid
suopww
x
v
randv
randv
speedwindv
tvtvtxtx
randvrandvtvtv

34. Knowledge assisted framework
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35. Slide: 35
Knowledge Assisted Analysis
Architecture

36. Slide: 36
Knowledge Assisted Analysis
Machine Learning - Evaluation

37. Slide: 37
Knowledge Assisted Analysis
 Experimental Dataset
 A set of 500 Images, belonging to the general category of
vacation images was assembled.
 The content was mainly obtained from Flickr online photo
management and sharing application and includes images
that depict cityscape, seaside, mountain and landscape
locations.
 Every image was manually annotated, i.e. after the
segmentation algorithm is applied, a single concept was
associated with each resulting image segment

38. Slide: 38
Knowledge Assisted Analysis
A subset of Database

39. Slide: 39
Knowledge Assisted Analysis
Comparison of Machine Learning techniques

40. Slide: 40
Knowledge Assisted Analysis
 From the results it can be seen that the combined use
of PSO optimisation technique with SOM results in
better classification accuracy compared to using the
latter alone.
 It can be noted that the performance of PSO classier
is better than the performance of SVM and GA
classifiers.
 Since, SVM's need large training data to accurately
discriminate between image classes.

41. Image retrieval and user relevance
feedback
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42. Slide: 42
User Relevance Feedback
Overview of Multimedia Retrieval System

43. Slide: 43
User Relevance Feedback
Relevance Feedback Framework

44. Slide: 44
User Relevance Feedback
 The database used in the experiment is generated
from Corel Dataset and consists of seven concepts
namely, building, cloud, car, elephant, grass, lion
and tiger
 The test set has been modelled for seven concepts
with a variety of background elements and
overlapping concepts, hence making the test set
complex.

45. Slide: 45
User Relevance Feedback
Example images from Corel Dataset

46. Slide: 46
User Relevance Feedback
Average Accuracy for 7 concepts
and 10 user interaction

47. Multi-concept search space
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48. Multi-concept framework
Slide 48
• High-level queries
“A tiger resting in the forest and guarding his territory”
• Mid-level features (context independent)
“Tiger”, “Grass”, “Rock”, “Water”,……

49. Multi-concept framework
• Mid-level features:
In a constrained environment with limited number of mid-
level features, the performance of classification algorithm
has found to be satisfactory
• High-level queries:
Open to subjective interpretation of the concepts and also
may involve more than one mid-level feature
Main objective:
• In this multi-concept framework, users are encouraged to
construct high level queries based on their preferences

50. Multi-concept framework
Slide 50

51. Mid-level feature extraction
Slide 51
• SVM Classifier
• SVM Light toolbox was used to generate semantic
labels
• CLD+EHD
• Multi-feature classifier (MF)
• Employs a mixture of 7 visual features.
• The visual features are merged using Multi-Objective
Learning (MOL)

52. Query space formulation
Slide 52
• Pre-processing stage: mid-level feature concept
detection
• Query formulation: users to construct a high-level
semantic information space

53. Query space visualisation
 Fisheye distortion technique
 Overview + focus
Slide 53

54. Query space visualisation
Slide 54
• Query space panel
• Concept map panel
• Concept chart panel

55. Experiments and Evaluation
Slide 55
• A 3500 image set collection
• From Corel dataset
• Natural images with many elements
• Foreground and background
• Rich semantic context
• Fully annotated
• 10 mid-level concepts
lion, water, grass, building, car, cloud, rock, tiger, elephant, flower
• 8 high-level concepts
flower fields, modern city view, rural garden, mountain view,
waterfalls, wild life, city street, boat

56. Comparison of results
 Retrieval of high level queries using the
proposed MCB framework
Slide 56

57. Comparison of results
 Retrieval of high level queries using SVM
classification
Slide 57

58. Comparison of results
 Content-based retrieval with RF
mechanism
Slide 58

59. Experiments and Evaluation
Slide 59

60. Experiment and Evaluation
Slide 60

61. Experiment and Evaluation
Slide 61
User 1
Landscape water, grass 0.58
Modern city building, cloud 0.8
Wild life lion, tiger, elephant 0.59
Rural garden flower, water, grass 0.9
User 2
Landscape water 0.23
Modern city building 0.71
Wild life lion, rock, grass, tiger, elephant 0.87
Rural garden flower 0.28
User 3
Landscape water, grass, cloud, car, elephant 0.59
Modern city cloud, building, car 0.91
Wild life lion, tiger, grass, elephant, rock 0.82
Rural garden flower, water, grass 0.88

62. Social media analysis
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63. Social Media Analysis
 Social media is the interaction among people
in which they create, share or exchange
information and ideas in virtual communities
and networks.
 Andreas Kaplan and Michael Haenlein define
social media as "a group of Internet-based
applications that build on the ideological and
technological foundations of Web 2.0
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64. Social Media Analysis
 Social media allows for the creation and
exchange of user-generated content.
 Social media differ from traditional or
industrial media in many ways, including
quality, reach, frequency, usability,
immediacy, and permanence.
25/07/14 64

65. Slide: 65
Textual and Visual Analysis
• Images are often accompanies with free-text
annotations, which can be used as
complementary information for content-based
classification
• The challenge is to extract entities from text
and classify them into an arbitrary set of
classes
Plansarsko lake
Shepherd in Bucegi
National Park

66. Slide: 66
Labeled
Corpora
(MUC,
BBN,ACE)
Textual and Visual Analysis

67. Slide: 67
Textual and Visual Analysis

68. Slide: 68
Annotated
Images
Binary
Segmentation
Masks
Segmentation
of Images
Feature
Extraction
Biologically
Inspired
Classifier
Training Model
Cic={Sky, Rock,..}
Semantic
Concept
Mapping
Targeted
Hypernym
Discovery
Wordnet
Wikipedia Classifier
Fusion
Labeled
Segments
Labeled
Entities
Ctc = {Person,
Landscape,..}
Visual analysis (KAA)Visual analysis (KAA)
Text Analysis (SCM+THD)Text Analysis (SCM+THD) FusionFusion
Use-case scenario

69. Slide: 69
Church of our Lady Mercy in Buje
BuildingBuilding
EXIF
Binary
mask
PSO
Largest region size
Use-case scenario

70. Slide: 70
 Map word to Wordnet concept
1. noun phrase
2. head noun
3. hypernym for noun phrase (with THD)
4. hypernym for head noun (with THD)
 Compute similarity with each of the classses
 Experiments carried out with Lin similarity measure
)(log)(log
)),((log*2
),(
21
21
21
cpcp
cclsop
ccsimL
+
=
The probability of encountering concept c
is usually estimated from a large corpus
)(cp
Semantic Concept Mapping

71. Slide: 71
Content-based analysis (KAA) restricted to classes
for which the classifier has been learnt
For text-based analysis (SCM/THD), the classes have
to be exhaustive - all entities are classified
Mapping from SCM/THD to KAA
 Perform intersection
between the individual
classifier results
 Select concept occupying
largest area on the image
Image
Class.
(KAA)
Text
Class.
Classifier Fusion

72. Indexing large-scale repositories
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73. Indexing Large-scale Repositories
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74. Indexing Large-scale Repositories
 The textual analysis block aims to generate a
list of named entities extracted from the
textual metadata associated with the input
video
 The pre-processing framework classifies the
tags into two general categories
 common-tags
 named entities
25/07/14 74

75. Indexing Large-scale Repositories
 Common tags correspond to either action,
country or associated with synset in WordNet
 Named-entity tags do not have a WordNet
synset and thus depend on extrenal resources
to contextualise them
 The objective of the pre-processing module is
to ensure the named entities are
disambiguated to enable a semantic similarity
search
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76. Indexing Large-scale Repositories
 Bag of Articles Classifier
 The input of a BOA classifier is a set of labelled
instances and a set of unlabelled instances (noun
chunks).
 Wikipedia article titles provide an unanimous
mapping between the labelled instance and a
wikipedia article
 Each article is described by its type (article, page,
disambiguation page, category page and so forth)
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77. Indexing Large-scale Repositories
 A BOA classifier requires a Wikipedia index
containing the following information about each
article
 term vectors with term frequencies
 out links and
 popularity ranking (for most frequent sense relevance
ranking)
 For geo-tagging adaptation, the textual analysis
block searches for geographical named entities in
the queries Wikipedia articles
 The location details are extracted with the help of
DBpedia using SPARQL end-point
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78. On-going research challenges
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79. VIT@MediaEval 2013
 Social Event Detection Task
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80. VIT@MediaEval 2013
25/07/14 80
 The geographical coordinates is an important component and
indicator of where an event has happened.
 The event clusters are nalised through the weighted occurrence of
tags among the distribution of media annotation

81. VIT@MediaEval 2013
25/07/14 81
 The system computes the similarity between
synset representing the tags and each of the
categories.
 We use Lin similarity measure to evaluate the
semantic distance between the synset and
category.

82. VIT@MediaEval 2013
 Placing Task
25/07/14 82

83. VIT@MediaEval 2013
 Dividing the globe into grids with a maximum
of 10,000 images per grid . Starting from an
initial grid that spans the entire globe,
recursively subdividing grids into smaller
ones once the threshold is reached.
25/07/14 83

84. VIT@MediaEval 2013
25/07/14 84
0
5
10
15
20
25
30
35
1 10 100 500 1000
Series1 0.74 3.9 15.24 26.3 30.14

85. Future research directions
25/07/14 85

86. Future Research Directions
 MediaEval is a multimedia benchmarking
initiative that offers tasks and datasets to the
research community that emphasize the
human and social aspects of multimedia.
 In 2014, MediaEval is offering eight classic
tasks and three Brave New Tasks.
 http://www.multimediaeval.org/mediaeval2014/
25/07/14 86

87. Future Research Directions
 ImageCLEF 2014
 ImageCLEF organizes four main tasks to
benchmark the challenging task of image
annotation for a wide range of source images and
annotation objective, such as general multi-
domain images for object or concept detection, as
well as domain-specific tasks such as visual-
depth images for robot vision and volumetric
medical images for automated structured
reporting.
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88. Future Research Directions
 The tasks address different aspects of the annotation
problem and are aimed at supporting and promoting
the cutting-edge research addressing the key
challenges in the field, such as multi-modal image
annotation, domain adaptation and ontology driven
image annotation.
 http://www.imageclef.org/2014
25/07/14 88

89. Thank you!!!
Q & A
25/07/14 89