pgdip-project-report-final-148245F

Visuo - A Deep Learning Video Search
Engine
V B Wickramasinghe (148245F)
Department of Computer Science and Engineering
University of Moratuwa
CS5999 Post Graduate Diploma Project Report
Master of Science in Computer Science
February 2015

Declaration
I declare that this is my own work and this PG diploma project report does not incorpo-
rate without acknowledgment any material previously submitted for degree or Diploma
in any other University or institute of higher learning and to the best of my knowledge
and belief it does not contain any material previously published or written by another
person except where the acknowledgment is made in the text.
Also, I hereby grant to University of Moratuwa the non-exclusive right to reproduce
and distribute my dissertation, in whole or in part in print, electronic or other medium. I
retain the right to use this content in whole or part in future works (such as articles or
books).
Signature: ..................................
Name: V B Wickramasinghe
Date: ..............................
I certify that the declaration above by the candidate is true to the best of my
knowledge and that this report is acceptable for evaluation for the CS5999 PG Diploma
Project.
Signature of the supervisor: ...............................
Name: Dr. Daya Chinthana Wimalasuriya
Date: ..............................

Abstract
Video is one of the main forms of storage of human knowledge at present with the trend
set to continue in to the foreseeable future. The importance of multimedia as well as
the machines that create, transport and store it for us is also growing by the day, with
most aspects of our lives such as learning, leisure, work etc., becoming more and more
dependant on these. This makes machine comprehension of video and other multimedia
content an important research area, because without the help of the machines we could
no longer make good use of the growing resource of multimedia. Content based video
retrieval tries to address a part of the problem by teaching machines how to retrieve
videos based on human queries at least with a partial understanding of the contents
within. This research is dedicated to understanding the various aspects of the problem
of content based video retrieval as well as the available solutions. The end goal is to
build Visuo, a deep learning video search engine.

Table of contents
List of figures ix
List of tables xi
Nomenclature xiv
1 Introduction 1
1.1 The Significance of Understanding Video Content . . . . . . . . . . . 1
1.1.1 The Problem Statement . . . . . . . . . . . . . . . . . . . . . 2
1.2 Introduction to Visuo . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Report Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Literature Survey 5
2.1 Content Based Video Retrieval . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 Structure Analysis . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.2 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.3 Learning from Video . . . . . . . . . . . . . . . . . . . . . . 10
2.1.4 Querying . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.5 Semantic Gap . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Deep Learning for Video . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.1 A Brief Survey of Deep Learning Techniques . . . . . . . . . 12
2.2.2 Deep Learning from Video . . . . . . . . . . . . . . . . . . . 13
2.2.3 Visual Representations to Text Translation . . . . . . . . . . . 18
3 Research Methodology 21
3.1 Scope and Goals of Research . . . . . . . . . . . . . . . . . . . . . . 21
3.1.1 Develop and Train the Visuo Large Scale CNN . . . . . . . . 21
3.1.2 Develop the Visuo Video Classification Engine . . . . . . . . 21
3.1.3 Develop and Train the Visuo Video Description Generator . . 22
3.1.4 Develop the Visuo Video Indexing Engine With Query UI . . 22
3.1.5 Large Scale Deployment of Visuo Video Indexing Engine With
Query UI . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.1.6 Develop the Visuo Speech Recognition Engine . . . . . . . . 22

viii Table of contents
3.1.7 Add Audio Indexing Support for the Visuo Video Indexing Engine 22
3.2 Proposed Architecture of Visuo . . . . . . . . . . . . . . . . . . . . . 23
3.3 Tentative Project Timeline and Deliverables . . . . . . . . . . . . . . 24
References 25

List of figures
1.1 Growth trend of internet video . . . . . . . . . . . . . . . . . . . . . 2
2.1 Generic framework for visual content-based video indexing and re-
trieval. Image source [24] . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Hierarchical video representation. Image source [15] . . . . . . . . . 6
2.3 Alexnet CNN. Image source [30] . . . . . . . . . . . . . . . . . . . 13
2.4 Training DAEs. Image source [22] . . . . . . . . . . . . . . . . . . . 14
2.5 Spatio-temporal feature extraction using a 3D CNN. Multiple 3D convo-
lutions can be applied to contiguous frames to extract multiple features.
The sets of connections are color-coded so that the shared weights are
in the same color. Image source [26] . . . . . . . . . . . . . . . . . . 16
2.6 Spatio-temporal feature extraction using a multi stream CNN. Image
source [47] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.7 LRCN architecture which is a combination of CNNs for individual
frame processing and an LSTM RNN for sequence learning. Image
source [12] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.8 LSTM-YT architecture described in [54] which includes a mean pooling
layer between the CNNs and the LSTMs. Image source [54] . . . . . 19
3.1 Visuo Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

List of tables
2.1 Key frame extraction approaches - summary . . . . . . . . . . . . . . 9
3.1 Tentative project timeline and deliverables . . . . . . . . . . . . . . . 24

Nomenclature
Acronyms / Abbreviations
BP Back-Propagation
CD Contrastive Divergence
CEC Constant Error Carousal
CNN Convolutional Neural Network
CRF Constant Rate Factor
DAE Deep Auto Encoder
DBN Deep Belief Network
DL Deep Learning
DNN Deep Neural Network
FNN Feed Forward Neural Network
GPU Graphics Processing Unit
HMM Hidden Markov Model
LRCN Long-term Recurrent Convolutional Networks
LSTM Long Short Term Memory
PCA Principal Component Analysis
RNN Recurrent Neural Network
ROI Region Of Interest
SIFT Scale-Invariant Feature Transform
SL Supervised Learning
SVM Support Vector Machine

xiv Nomenclature
UI User Interface
UL Unsupervised Learning
VKFE Visuo Key Frame Extractor
VRNN Visuo Recurrent Neural Network
VLSCNN Visuo Large Scale Convolutional Neural Network
VQUI Visuo Query User Interface
VSRE Visuo Speech Recognition Engine
VTU View Tuned Units
VVCE Visuo Video Classification Engine
VVDG Visuo Video Description Generator
VVIE Visuo Video Indexing Engine

Chapter 1
Introduction
1.1 The Significance of Understanding Video Content
According to statistics 100 hours of video are uploaded to YouTube.com every minute
while over 6 billion hours of video are watched each month on it [4]. With the general
availability of high quality video recording technology as well as the rapid adoption of
smartphone around the world, more and more users are creating and consuming video
content instead of more traditional forms of content such as text and images on the
internet. Video is the main form of storage of human knowledge at present with the
trend set to continue for even more video content creation in the future(Fig. 1.1).
Humans embrace video content because of brains capacity to process visual informa-
tion extremely fast [43]. But the computers which help transfer and consume this huge
amount of video data are mostly oblivious to the actual content they are transferring.
Though machine vision field has made rapid advances in recent years it still lags behind
when it comes to processing large amounts of video data to extract meaning when
compared to humans. As the second chapter of this report reveals traditional machine
vision techniques are mostly dependent on hand designed features and generally not
scalable enough to be applied to the problem of understanding video content. But the
benefits of machines being able to learn from the most comprehensive source of human
knowledge can be extensive. From automatic transcription for hearing or visually im-
paired to robot vision, from video search engines to automatic surveillance and internet
video content filtering, possibilities are enormous. Also understanding video content
might be a stepping stone for one of the ultimate goals of computing - Artificial General
Intelligence.
Our ambition in conducting this research is to put a step in the right direction in
machine comprehension of video content. Therefore it will focus on understanding
a video enough to be able to index it for a video search engine. Though this seems
somewhat simpler than the original problem of machine comprehension of video,
Solving the indexing problem may lead to better understanding of complexities as well

2 Introduction
Fig. 1.1 Growth trend of internet video
as techniques required in achieving the ultimate goal of machine comprehension of
video.
1.1.1 The Problem Statement
This research will address the problem of automatically indexing a given set of videos
based on their visual and auditory content. A machine learning algorithm shall generate
textual representations of each logical segment in the videos, enabling a traditional
text based information retrieval system to index the content of the videos. This would
facilitate text based queries on top of this index sent via a search engine application
interface. Users of the system should be able to find time of occurrence of a textually
described interesting event inside the video(s) and retrieve video(s) based on relevance
to the specified event.
1.2 Introduction to Visuo
The end goal of this research is to build Visuo, a deep learning video search engine
that lets its users search within a given video(s) for interesting visual or auditory events.
Examples of such events could be a particular dance move in a music video, the place
where convolutional neural networks are taught in a computer science lecture video or
the place where Beethovens moonlight sonata is played in the background of a movie.

1.3 Report Outline 3
There has been considerable research into video indexing and retrieval previously as
described in the chapter 2 of this report. Also many attempts have been made to build
commercial/non-commercial video search engines. For example Blinkx a commercial
multimedia discovery search engine can answer user queries about video based on its
indexed built on video meta data and speech and audio recognition. Although meta data
approach works to some extent at large scales its abilities are very limited in scenario
such as a personal or organizational video collection where meta data is limited. Our
goal in building Visuo is to try to make machines understand about videos by deep
learning or semantic learning on the contents within it. This should make it a more
helpful tool in large scale video search on sites such as youtube.com or a personalized
setting such as your multi-terabyte hard drive.
1.3 Report Outline
The rest of the sections in this report are organized as follows. Chapter 2 surveys current
literature for current techniques and possible future approaches on video retrieval and
deep learning fields. Specifically section 2.1 in chapter 2 covers areas of content based
video retrieval literature on techniques that are for any video search engine such as key
frame extraction, feature extraction, querying etc. Section 2.2 of chapter 2 explores
cutting edge deep learning field for techniques that are of interest for video processing
and indexing tasks. Chapter 3 clearly defines the scope of this research as well as its
goals to achieve within the period of this Master of Science in computer science project.
It also describes the proposed architecture of Visuo search engine which we intend to
implement step by step. Those steps and the tentative timeline of the project is presented
in the last section of the chapter 3.

Chapter 2
Literature Survey
2.1 Content Based Video Retrieval
Content based video retrieval has attracted lots of interest among researchers because
of its importance in many applications of computer science. Videos are very dense
in information, a feature that makes content based video retrieval a hard research
problem, leaving a trail of research that spans more than two decades. Therefore this
section would be dedicated to covering conventional knowledge in video processing and
retrieval acquired over a long time that doesn’t involve DL approaches. According to
the in depth survey by Hu et al. on the subject [24] videos have following characteristics
1) much richer content than individual images; 2) huge amount of raw data; and 3) very
little prior structure. They further introduce a generic framework for visual content-
based video indexing and retrieval(Fig. 2.1) components of which is used to guide the
discussion on this section.
2.1.1 Structure Analysis
First step in the video processing pipeline is the structure analysis in which each video to
be indexed is logically segmented into manageable chunks. These chunks can be defined
as a hierarchy of structures namely video clips, scenes, shots and frames(Fig. 2.2). A
frame is the smallest unit of this hierarchy which represents a single still image in a
sequence that creates a shot, scene or a video clip. Shots are a sequence of frames
captured by a single camera within a single start and stop operation. They represent
a natural boundary from which the segmentation can begin. Scenes capture the most
semantic meaning in this hierarchy. A Scene is a sequence of semantically related and
temporally adjacent shots depicting a high-level concept or story [10].
Video segmentation using shot boundary detection is a heavily researched area in
video processing field as evidenced by [5, 24, 48, 49]. According to [24] shot boundary
detection involves three steps 1) feature extraction where several features useful for
shot boundary detection such as color histogram, edge change ratio, motion vectors,

6 Literature Survey
Fig. 2.1 Generic framework for visual content-based video indexing and retrieval. Image
source [24]
Fig. 2.2 Hierarchical video representation. Image source [15]

2.1 Content Based Video Retrieval 7
scale invariant feature transform, corner points, information saliency map could be
extracted. In step 2) similarity measurement - the extracted features are compared using
different measures such as 1-norm cosine dissimilarity, the Euclidean distance, the
histogram intersection, and the chi-squared similarity. In final step 3) detection - the
shot boundaries can be detected by utilizing two strategies. First is the threshold based
approach where similarity measurements are compared with a predefined threshold to
detect shots and the second is to use machine learning to identify shot boundaries using
aforementioned features as input vectors. A more recent treatment of shot detection
subject is [25] where the authors discuss shot detection under gradual transition as well
as using multiple features for detection.
Next the key frame extraction can be performed on segmented shots to extract
representative frames off the set of frames in the shot that would minimize error rate and
maximize compression ratio for the shot [24]. According to [24] available approaches to
key frame extraction can be classified in to six categories. In 1) sequential comparison-
based approach sequence of frames in a shot are compared with last extracted key frame
for the shot to extract the next key frame. In 2) global comparison-based approach
the key frames are extracted based on minimizing a global objective function for all
extracted key frames of the shot. In 3) reference frame-based approach a single reference
frame is generated for the given shot and all available shots for the frame are compared
with the reference frame. Key frames are extracted when the comparison method
reaches a certain threshold. In 4) clustering based approached all frames in shot are
clustered based on a clustering algorithm and frames closer to the centers of the clusters
are chosen as the key frames. In 5) curve simplification-based approach represent each
frame in a shot as a point in a feature space and try to find a set of points that best
describe the curve. In 6) object/event-based approach key frame extraction and object
event detection is combined together to find key frames that contain interesting objects
or events. A summary of advantages, disadvantages and implementations of all of these
key frame extraction approaches is provided in Table 2.1.
Generally once shot detection and key frame extraction is complete the next step in
structure analysis is the scene detection. Scene detection methods can be categorized
in to three approaches according to [24]. In 1) key frame based approach shots are
organized into scenes based on features extracted from key frames within temporally
close shots. More similar those key frames are more likely two shots are from the same
scene. In 2) audio and vision integration-based approach the shots are categorized into
scenes considering both visual and audio features. If in particular shot boundary both
audio and visual features change dramatically then the shot boundary is considered
as a scene boundary as well. This kind of scene segmentation approach is further
expanded upon by [15] where the authors categorize scene segmentation approaches
using features used such as visual-based, audio-based and text-based. In 3) Background-
based approach scenes are segmented based on detecting similar backgrounds in key

8 Literature Survey
frames. For a more recent look at scene segmentation subject as well as evaluation
strategies [15] serves as a good resource.
2.1.2 Feature Extraction
After structure analysis and various segmentation steps the generated segments needs
to be analyzed to extract various features in order to index a video. According to [24]
these features can be broadly classified into three categories. 1) Key frame features
which are static, 2) Object features and 3) motion features.
Key frame features can be further categorized into 1) color based features which
include color histograms, color moments, color correlograms, a mixture of Gaussian
models, etc. Extraction of these features depends on the application dependent color
space selection such as RGB, HSV, YCbCr etc. These can be used on entire images
or on parts of it and they are easy to extract in terms of computational complexity.
But they lack the capability to describe texture and shape of a key frame. [6] is an
example of a video search engine that uses color based features for indexing. 2) Texture
based features describe different surfaces of objects in a key frame. 3) Shape based
features describe shapes of different objects in key frame. Object features include the
dominant color, texture, size, etc., of the image regions corresponding to the objects.
For example the objects could be human faces. These are computationally expensive to
extract and largely limited to detecting several predefined object types such as faces
or humans. Motion features are features resulting from either camera movement or
object movement in a video. Object movement features can be further divided into
statistics-based, trajectory-based, and objects’ spatial relationships-based features.
A general observation that can be made about different categories of features
described based on [24] is that most of them are computationally expensive, hand
designed and mostly application/domain/object specific. When implementing a general
purpose video search engine those limitations may make it harder to scale because of
effort involved in extracting features. Therefore this survey do not go into much detail
about these traditional methods of feature extraction. A recent trend in machine learning
community is to automatically learn useful features instead of hand designing them [3].
Section 2.2 explores these novel approaches in feature extraction for video.

2.1 Content Based Video Retrieval 9
ApproachAdvantagesDisadvantagesMajorimplementations
Sequentialcomparison-basedsimplicity,intuitiveness,low
computationalcomplexity,adap-
tationofthenumberofkey
framestothelengthoftheshot
keyframesrepresentlocalproper-
tiesoftheshot,Theirregulardis-
tributionanduncontrollednum-
berofkeyframes,Redundancy
whensamecontentsareappear-
ingrepeatedlyinthesameshot
[36,58,60,61]
Globalcomparison-basedKeyframesreflecttheglobal
characteristicsoftheshot,Con-
trollablenumberofkeyframes,
Moreconciseandlessredundant
keyframes
computationallyexpensive[9,11,14,32,42]
Referenceframe-basedeasytounderstandandimple-
ment
dependenceonthereference
frame
[16,51]
Clustering-basedusegenericclusteringalgorithms,
globalcharacteristicsofashot
isreflectedintheextractedkey
frames
acquisitionofsemanticmeaning-
fulclustersisdifficult,sequential
natureoftheshotcannotbenatu-
rallyutilized
[17,18,59]
Curvesimplification-basedsequentialinformationiskepthighcomputationalcomplexity[8]
Object/event-basedextractedkeyframesaresemanti-
callyimportant
object/eventdetectionstrongly
reliesonheuristicrulesspecified
accordingtotheapplication
[29,34,35,50]
Table2.1Keyframeextractionapproaches-summary

10 Literature Survey
2.1.3 Learning from Video
The most important part of the video retrieval process is the automatic mining, classifica-
tion and annotation of the videos that are to be retrieved from based on user queries. All
of these tasks are heavily dependent on the earlier steps of structure analysis and feature
extraction. According to [24] Video mining refers to the process of automatically
identifying patterns in structure, objects and events in a video using machine learning
techniques. There are few strategies to go about mining videos. Object mining refers
to identifying the same object in different scenes, shots or key frames of a video. This
requires identifying objects under different visual transformations in different frames of
the video which is hard. A more recent development computer vision area, supervoxel
methods [57] allows efficient segmentation of video based on objects which makes
mining for objects easier. Other strategies for video mining include special pattern
detection such as human actions, pattern discovery, association mining between objects
or events and tendency mining.
Video classification refers to automatic categorization of videos based on their con-
tent. In [24] classification can be further categorized into edit effect classification, genre
classification, event classification, and object classification. Genre based classification
is particularly difficult because of the semantic gap between the low level feature of
the video and the semantic class it belongs to. To overcome this problem a rule based
or machine learning approach can be utilized. A recent convolutional neural network
based approach [28] achieved significant results over existing benchmarks for video
classification. Which hints at the ability of DL approaches to overcome semantic gap.
Video annotation is classification or labeling of individual segments of a video
into different semantic classes such as person, cat, walking, dancing etc. With different
features extracted during the feature extraction phase classifiers or detectors are trained
to detect each higher order semantic concept from the extracted features. A recent
study [27] on the subject explores the possibility of deploying different detectors at
different locations of video or segment to reduce computational cost.
2.1.4 Querying
According to [49] there are three main types of queries that a user can execute on a
content based video search engine. 1) Query by keyword is the most intuitive method
for human to interact with a search engine as it allows to express concepts with complex
semantics. But this is also the most difficult type of query to support because of the
semantic gap between the systems knowledge of the low level features and the user
query with complex semantics. Because of this reason most of the commercial video
search engines such as blinkx [2] support keyword type queries based on an index built
using audio transcribed text of videos. The newer methods which generate textual
representations of various objects, events and patterns in a video based on DL has

2.2 Deep Learning for Video 11
been proposed and will be covered in section 2.2. 2) Query by example technique
allows a user to submit a sample video, image or a sketch to the search engine which in
turn transforms the sample into a feature representation which it can compare with the
already built feature index. 3) In Query by concept paradigm the user is allowed to
select between predefined concepts already available to the search engine through its
index.
2.1.5 Semantic Gap
Human representations of certain concepts are generalized to a level where even with
ambiguous natural language descriptions of those concepts can convey meaning to
another human being. But when those concepts are transformed into a machine level
description the generic nature as well as the information content is lost most of the
time. In machine learning literature this is known as semantic gap [13]. For example
in [13] the authors try to address the problem of semantic richness of user queries
executed on simple feature(shape, color, patterns) based representations of multimedia
retrieval systems. Because the rich queries negatively affect the retrieval accuracy and
performance.
The recent resurgence in the field of neural networks and DL has provided computer
science a new path to explore in minimizing semantic gap [1]. The ability of deep neural
network architectures to automatically learn features instead of being hand crafted as
well as their ability to generalize has meant that now more and more of traditional
computer vision and machine learning benchmarks are being won by these kind of
algorithms instead of techniques such as SVMs [46]. Many neural network architec-
tures have been explored in research literature for different kinds of machine learning
problems from convolutional neural networks that learn higher order representations of
images to recurrent neural networks that detect complex patterns in temporally varying
data.
Next section explores this trend towards deep architectures in relation to video
retrieval space. Our intention is to explore novel ideas in video(multimedia) indexing
using deep neural network architectures.
2.2 Deep Learning for Video
This section explores scientific literature to understand DL techniques as well as their
application towards learning from video and indexing them. The subsection 2.2.1
surveys DL techniques in general to provide the necessary background as well as to
highlight the state of the field. Next the subsection 2.2.2 studies significant contributions
in recent research literature for the application of DL techniques to process and under-
stand video. Finally subsection 2.2.3 studies DL techniques which allow translation

of visual/auditory representations to text as this is an important task for indexing in a
search engine.
2.2.1 A Brief Survey of Deep Learning Techniques
According to a recent survey by Schmidhuber [46] first inspirations of DL structures
span as far back as 1962 when Hubel et al investigated the visual cortex of cats explore
its cell structure. Then the most important milestone for learning in DNNs occurred
with the invention of back-propagation(BP) algorithm in [33]. But the term back-
propagation in the context of neural networks only gets coined in [44]. BP is a dynamic
programming solution where the weight of particular neuronal connection is updated
using the backward propagated error(difference between the expected and the obtained
output) using chain rule and error derivatives. The advantage of this technique is that
global contribution of a particular weight towards the error in output of the network can
be calculated with the use of local gradients. [44] also serves as an early example and
interpretation of representation learning or automatic feature learning in FNNs. Another
important technique a convolutional neural network(CNN) was trained using BP by
Lecun et al in [31] to achieve state of the art results in handwriting recognition for
postal zip codes back in 1989. Key features of a CNN are the weight sharing based
kernels and under sampling layers that help in optimal feature extraction. At the time of
writing CNNs trained using BP [30, 53] continues to dominate various image processing
benchmarks and competitions such as ImageNet classification competition which used
to be dominated by traditional pattern recognition techniques [45]. The trend was started
by Krizhevsky et al with a neural network now known as alexnet(Fig. 2.3), which has
60 million parameters and 500,000 neurons, consists of five convolutional layers, some
of which are followed by max-pooling layers, and two globally connected layers with a
final 1000-way softmax [30]. Important fact to note here is that this renaissance of DL
is actually inspired by the exponential improvement in parallel processing hardware i.e.
GPUs which has helped in achieving speedups of as much as 50 times when training a
large scale neural network [46]. GPUs provide excellent processing medium for DNNs
because of their extreme parallel processing capabilities as well as lower cost compared
to other high end alternatives.
Though FNNs has seen good results with limited set of tasks in the 1980s, it was
known that recurrent neural networks(RNN) had the most computational power
if trained properly with ability to model any kind of computation [46]. But deep
RNNs had problems in training using the two most common techniques to train them,
back-propagation through time(BPTT) and Real Time Recurrent Learning(RTRL). In
1991 fundamental problem with training deep RNNs, the problem of vanishing and
exploding gradients was observed according to [46]. The solution to this problem
was the invention of long short term memory(LSTM) [23] networks. LSTMs solve
the exploding or vanishing gradient problem by introducing the concept of a LSTM

Fig. 2.3 Alexnet CNN. Image source [30]
memory cell which is constructed using a combination of linear and multiplicative
units. It allows constant error propagation through what is called a constant error
carousal(CEC) within it. The memory cell also has an input gate which controls when a
signal is allowed to affect the internal state of the cell and an output gate which controls
which error signals are allowed to propagate within the cell. These gates allow a LSTM
cell to learn when to modify, pass on or forget a memory. LSTM based RNNs are good
at sequence modeling tasks which require the use of short term internal state. The first
remarkable results achieved with LSTMs are reported in [19] where an LSTM based
RNN trained on faster hardware of 2009 set the state of the art in connected handwriting
recognition overtaking existing HMM based approaches. They have also been utilized
in speech recognition and statistical machine translation tasks with some achieving
state of the art [52] performance. Use of LSTMs in language generative models will be
further discussed in sub section 2.2.3.
Two other techniques which are of interest in DL are Deep Belief Networks(DBN) [21]
and Deep Auto Encoders(DAE) [22]. DBNs are constructed by stacking layers of re-
stricted Boltzmann machines(RBM) to form a deep architecture. RBMs are a particular
form of energy based learning model where the learning corresponds to modifying its
energy function so that the functions shape has desirable properties. By training each
layer of RBM in a DBN using the efficient unsupervised training algorithm known
as contrastive divergence [20] the DBN can be initialized in a good neighborhood
before final supervised fine tuning of the whole network [21] starts. DAEs are stacks of
denoising autoencoders that help in constructing low dimensional representations of
input data. Because the training of DAEs heavily depends on good initial weights, [22]
suggests an initialization strategy based on layer-wise unsupervised pre-training of a
deep net of RBMs after which the layers of RBMs are unrolled to form a DAE(Fig. 2.4).
The DAE then can be fine tuned using BP for the desired task.
2.2.2 Deep Learning from Video
Although there has been previous attempts at using neural networks [39] to mine and
classify video, the first truly representation learning [7] DNN trained for video mining

Fig. 2.4 Training DAEs. Image source [22]
we could find was presented by Mobahi et al in [38] referred to as the videoCNN. As
the name suggests videoCNN is a CNN trained to identify objects in images by first
pre-training it in a supervised manner on temporal coherence based objective. Temporal
coherence refers to the similarity between successive frames in a video. In videoCNN
the authors help the network learn pose invariant features by training it on pose varying
videos of the objects to be identified and forcing the representations of successive frames
to be closer together as the supervisory signal during training. Then the videoCNN
achieves good classification performance comparable to prevaili state of the art methods
such as SVM and VTU [56] which use hand designed features instead of learned deep
representations on COIL100 image dataset. Though this method extracts useful features
from video it still isn’t good example of mining objects or events in a given video as it
only uses video for training.
A more influential paper, multimodal deep learning [40] by Ngiam et al serves as
an excellent resource in explaining how to train models that learn good audio/visual
representations of video using deep learning. The authors train different models based
on DBN and DAE architectures for an audio/visual classification task using CUAVE [41]
and AVLetters [37] datasets. Here the audio for the datasets are processed into their
spectrograms with temporal derivatives, resulting in a 100 dimension vector with PCA
whitening. The video for the datasets were preprocessed to extract only the region-of-
interest (ROI) encompassing the mouth. Each mouth ROI was re-scaled to 60 × 80 pixels
and further reduced to 32 dimensions using PCA whitening. In the first phase of training,

separate RBMs are trained for separate audio, video and bimodel input of the set of
training videos. Then these are unrolled [22] to form three DAEs which reconstruct
original video and audio input given video only, audio only and both audio/video inputs.
Then the shared representation obtained in the DAE is used with a linear SVM classifier
to do the final classification of input video or audio. This setup results in excellent state
of the art performance for number/letter classification on the datasets using video/audio
where the network learns to make use of audio representation favorably in the video
classification task. The network learns to use shared representation to better classify
audio under noise as well. Though the training phase seems more complicated with
additional steps to extract ROI and do PCA whitening, this model proves that DL
based representations are far superior than using hand designed features for machine
understanding of multimedia.
As evidenced by [30, 53], at the time of writing CNNs are the dominating DL
technique for large scale image recognition tasks. The advantage of CNNs over con-
ventional image processing techniques such as SIFT as well as some of the other DL
techniques is that a single CNN can be trained to learn useful features from raw images
while also making classification judgments by itself at a scale of millions of param-
eters. This kind of end to end train-ability reduces the complexity of training while
increasing computational cost, which is advantages because computational power is
increasingly becoming a less expensive commodity. Therefore a lot of recent research
literature [12, 26, 28, 47] has been dedicated to the problem of extending CNNs to the
domain of video processing with the additional temporal dimension. According to our
own review results, the approaches taken for adapting CNNs for the temporal dimension
can be classified into three types.
1. In 3D convolution based approach the temporal dimension is handled in the
same way as the other dimensions of width and height of a frame(Fig. 2.5).
Convolution is performed on an extracted 3D feature map of a desired region
of the video using a 3D kernel. [26] presents a 3D CNN which is evaluated on
a task of human action recognition. Here the authors extract initial region of
interest using human detectors and generate multi channel 3D feature maps of
the extracted region. Then multiple layers of 3D convolution is performed on the
extracted feature maps separately using a 7x7x3 kernel with 2x2 sub-sampling
layers between the convolutional layers. The model achieves state of the art
performance on TRECVID dataset. The advantage of this approach seem to be
the way it naturally fits the 3 dimensional video data as a direct interpolation of
2D CNNs to the third dimension of video with very little modification to training
algorithm. However further study may be required to understand actual benefits
and practicality of this approach versus the other approaches available.

Fig. 2.5 Spatio-temporal feature extraction using a 3D CNN. Multiple 3D convolutions
can be applied to contiguous frames to extract multiple features. The sets of connections
are color-coded so that the shared weights are in the same color. Image source [26]
2. In multi stream convolution based approach two or more separate CNNs are
trained to mutually or individually learn a representation for the temporal infor-
mation between frames of a video. For example in [28] Karpathy et al train two
networks of this type. In late fusion network there are two columns of CNNs
training on frames 15 frames apart from each other combined at the fully con-
nected layer. In slow fusion network there are multiple CNN columns training on
overlapping four frame areas in combined area 10 frames wide combined slowly
at several layers of the network which each upper layer having access to more
information in the temporal dimension than the layer below. The slow fusion
model works well on the UCF101 dataset with classification accuracy 65% as
well as on the sports1m dataset the authors have prepared with a classification
accuracy of 60.9%. In another example for multi stream convolution based ap-
proach in [47] Simonyan et al train two CNNs one to learn spatial features of each
single frame and another to learn temporal features between consecutive frames
using feature vectors obtained through optical flow technique(Fig. 2.6). The
learned representations are fused together using the average or an SVM classifier
to obtain final classification. SVM fusion model achieves state of the art on the
UCF101 dataset overtaking hand designed models as well as the aforementioned
slow fusion model.
3. In CNN+RNN based approach a CNN is used to obtain representations of indi-
vidual frames a task for which they are good at, while the temporal representation
for the sequence of frames is learned by a specialized sequence processing mod-

Fig. 2.6 Spatio-temporal feature extraction using a multi stream CNN. Image source
[47]
Fig. 2.7 LRCN architecture which is a combination of CNNs for individual frame
processing and an LSTM RNN for sequence learning. Image source [12]
ule - an RNN using the representations generated by the CNN. These have the
advantage of being deep in spatial dimension as well as the temporal dimension
compared to the previous two approaches which are limited only to a select win-
dow of frames at a time step. [12] presents an excellent example of this kind of
network named long-term recurrent convolutional networks(LRCN)(Fig. 2.7). It
achieves classification accuracy of 82.9% on UCF101. Besides being a classifier
this network is able to perform as a generative model of video descriptions as
well. Therefore LRCN architecture is further discussed in sub section 2.2.3.
Compared to DAE based architecture presented by Ngiam et al in [40] CNN based
techniques have the advantage of being able to extract, process and classify features
using the same DL network without requiring any other technique in the feature extrac-
tion phase or classification phase. CNNs are less complicated in training while also

providing more accuracy in classification. Considering these merits we decided to base
the DL architecture of Visuo on CNNs. In the very dynamic DL field there may be
many other combinations of networks possible for use in DL from video. Our intention
was to present the state of the art in summary to be useful for developing a content
based video retrieval system.
2.2.3 Visual Representations to Text Translation
Another area of research that has experienced a resurgence because of deep learning
techniques is visual-auditory data to text transcription/translation. This area of research
is in part driven by its usefulness for supporting text queries on multimedia search
engines. Here we review two very recent inter-related breakthrough papers on the
subject that present a CNN+RNN based DL technique(LRCN) for visual/auditory
representations to text translation task.
As discussed in sub section 2.2 Donahue et al in [12] present their LRCN archi-
tecture which is a DL model in spatial using CNNs as well as temporal dimensions
using LSTM RNNs. They evaluate this architecture on three tasks activity recognition
in video, image description generation and video description generation for which they
use a CRF generator in place of a CNN. In a followup paper [54] published by the
same research group, they suggest and extension to the architecture used for the video
description generation task where the CRF generator in [12] is replaced by a set of
CNNs and a mean pooling layer. The CNNs are provided with a sample of frames
from the input video. Then mean pooling layer generates a fixed length one hot vector
representation of the entire video taking the mean of output of the second fully con-
nected layer of each CNN. This vector is then fed to the text generating LSTM net to get
the final video description text. The final architecture which they call LSTM-YT(Fig.
2.8) is trained using combinations of Flickr30k and COCO2014 image description
datasets or without them and fine tuned on the Microsoft Research Video Description
Corpus that contains thousands of clips of youtube videos with descriptions. The trained
network outperforms previous models based on BLEU and METEOR scores and human
evaluation, setting the state of the art.
Considering the DL merits of CNNs as well as the performance facts presented here
about LSTM-YT architecture, We are of the opinion that a LSTM-YT based architecture
would work well for the task of feature extraction and text generation phases of Visuo.

Fig. 2.8 LSTM-YT architecture described in [54] which includes a mean pooling layer
between the CNNs and the LSTMs. Image source [54]

Chapter 3
Research Methodology
3.1 Scope and Goals of Research
In chapter 2 of this report we explored the broad fields of research of content based
video retrieval and DL. Because of this broadness we need to clearly define the scope
of our research so that it will fit into the time constraint of one year. To cover much
ground as possible, we intend to define several levels of goals with increasing scope
and complexity. Achievement of each goal should mark a way point in our research
where we could conclude the research or continue on to next goal with time permitting.
The next few sub sections will describe these goals in order of ascending scope.
3.1.1 Develop and Train the Visuo Large Scale CNN
First goal of this research is to develop and train the Visuo Large Scale CNN(VLSCNN)
which is the foundation for all the visual recognition tasks in Visuo. This task requires
experiments to understand training phase of CNNs, high performance implementa-
tion techniques on GPUs and large scale deployment. Once developed and trained,
optimization could be carried out in parallel to other goals of this research.
Evaluation of existing deep learning frameworks will be carried out before any
implementation of the net.
3.1.2 Develop the Visuo Video Classification Engine
This is one of the most important and complex goals of this research. The tasks involved
with developing Visuo Video Classification Engine(VVCE) are two fold. 1) To research
and implement a suitable key frame extraction module(Visuo Key Frame Extractor-
VKFE) to make sure that the extracted frames are representative enough of the video so
that it can be classified using VLSCNN. As summarized in the table 2.1 there are many
techniques which can be utilized in extracting key frames. We wish to explore sequential
comparison based and global comparison based approaches in guiding our research

22 Research Methodology
and development in a suitable key frame extraction technique. We also believe that
this would be one of the differentiating factors between our approach and the current
research in DL techniques for video processing. 2) Implement the integration between
CNN developed earlier and the video processing engine with key frame extraction to
complete VVCE.
3.1.3 Develop and Train the Visuo Video Description Generator
Another high complexity component of our research is the Visuo Video Description
Generator(VVDG). This component will be implemented on top of VVCE with two
modifications to its architecture. 1) Modify the VLSCNN to match the design presented
in Fig. 2.8. 2) Build, Integrate and train the additional LSTM based RNN(Visuo RNN)
required to complete the LSTM-YT architecture.
3.1.4 Develop the Visuo Video Indexing Engine With Query UI
Visuo Video Indexing Engine(VVIE) and Visuo Query UI(VQUI) should be simpler to
implement as there are plenty of open source indexing and query engines available to
index simplified text based representations of video produced by VVDG.
3.1.5 Large Scale Deployment of Visuo Video Indexing Engine With
Query UI
This should require some time and effort because of the complexities involved in
supporting a server runtime with automated indexing support and many number of
users at a time. A cloud computing environment would be ideal as it provides the most
flexibility.
3.1.6 Develop the Visuo Speech Recognition Engine
Developing Visuo Speech Recognition Engine(VSRE) should be easier given that there
are few good open source libraries available for speech recognition such as CMU
Sphinx [55]. This component should generate textual representation of the audio track
of the video to be indexed by Visuo.
3.1.7 Add Audio Indexing Support for the Visuo Video Indexing
Engine
After developing VSRE its textual representation of audio track of a given video can be
indexed by VVIE.

3.2 Proposed Architecture of Visuo 23
Fig. 3.1 Visuo Architecture
3.2 Proposed Architecture of Visuo
Fig. 3.1 shows the proposed architecture of Visuo after all the goals mentioned in
the section 3.1 is achieved to completion. When a user wants to search through his
collection of online or offline videos he needs to submit them to be indexed by Visuo.
After submitting video will be processed by the VKFE and the VSRE. VLSCNN will
work on key frames produced by the VKFE to generate classifications to be indexed
and and suitable representations of them to be processed by the VRNN. VVIE will take
text generated by both VSRE and VRNN to index them to be retrieved later based on
fields such as visual features, audio features, events etc. Once the users videos have
been indexed, the index can be queries using text queries on VQUI.

24 Research Methodology
3.3TentativeProjectTimelineandDeliverables
DeliverableNoDeliverableStartEndDurationDependencies
1VLSCNN10/02/201510/04/20152monthsNone
2VVCE10/04/201510/07/20153months1
3VVDG10/07/201510/10/20153months2
4VVIE&VQUI10/07/201510/08/20151monthNone
5LargeScaleDeploymentofVVIE&VQUI10/08/201510/09/20151month4
6VSRE10/09/201510/10/20151monthNone
7AddAudioIndexingSupportfortheVVIE10/10/201510/11/20151month4
8Thesis10/10/201510/12/20152monthsAll
Table3.1Tentativeprojecttimelineanddeliverables

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