50320140502001 2

International Journal of Information Technology & Management Information System (IJITMIS), ISSN 0976 –
6405(Print), ISSN 0976 – 6413(Online), Volume 5, Issue 2, May - August (2014), pp. 01-14 © IAEME
1
COLOR BASED VIDEO RETRIEVAL USING BLOCK AND GLOBAL
METHODS
Smita Chavan
Information Technology Department, Government Engineering College,
Osmanpura, Aurangabad, India
ABSTRACT
Interacting with multimedia data and video in particular, requires more than
connecting with data banks and delivering data via networks to customer’s homes or offices.
We still have limited tools and applications to describe, organize, and manage video data.
The fundamental approach is to Index video data and make it a structured media manually
generating video. Color features are key-elements which are widely used in video retrieval,
video content-analysis, and object recognition. In the present study we are using the video
retrieval technique, based on color content, the standard way of extracting a color from an
image is to generate a color histogram. Therefore, the core research in content-based video
retrieval is developing technologies to automatically parse video, audio, and text to identify
meaningful composition structure and to extract and represent content attributes of any video
sources. A typical scheme of video-content analysis and indexing, as proposed by many
researchers, involves four primary processes: feature extraction, structure analysis,
abstraction, and indexing. Each process poses many challenging research problems. The main
processes in a proposed system includes search image, apply two methods for color feature
extraction, find matching videos as compared with image input given, calculation of distance
metrics using Euclidean distance measurement.
Keywords: Absolute Difference, Distance Metrics, Euclidean Distance, Feature Extraction,
Similarity Measurement.
1. INTRODUCTION
Content-Based video search is according to the user-supplied in the bottom
Characteristics, directly find out images containing specific content from the image library
the basic process: First of all, do appropriate pre-processing of images like size and image
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY &
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ISSN 0976 – 6405(Print)
ISSN 0976 – 6413(Online)
Volume 5, Issue 2, May - August (2014), pp. 01-14
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transformation and noise reduction is taking place, and then extract image characteristics
needed from the image according to the contents of images to keep in the database. When we
retrieve to identify the image, extract the corresponding features from a known image and
then retrieve the image database to identify the images which are similar with it, also we can
give some of the characteristics based on a query requirement, then retrieve out the required
images based on the given suitable values. In the whole retrieval process, feature extraction is
essential it is closely related to all aspects of the feature, such as color, shape, texture and
space. A color image is a combination of some basic colors. In MATLAB breaks each
individual pixel of a color image termed true color down into Red, Green and Blue values.
We are going to get as a result, for the entire image is 3 matrices, each one representing color
features. The three matrices are arranging in sequential order, next to each other creating a 3
dimensional m by n by 3 matrixes. An image which has a height of 5 pixels and width of 10
pixels the resulting in MATLAB would be a 5 by 10 by 3 matrixes for a true color image. For
improved retrieval performance, results from the different content retrieval methods may be
combined. Fusion of multimedia search results is query-dependent, and an area of ongoing
research. Besides merging results from different content retrieval methods, when retrieving
whole programs, it is necessary to combine shot-level results from within the program. There
has not been much work in this area, and approaches consist of assigning binary values to
each shot and aggregating these to the program level, averaging the scores of all shots for a
particular feature, using the maximum score of a shot in a program, or simply using the
central shot in a program to represent the whole video[1].Overall paper contains section I
intoduction,section II history, section III methods, section IV implementation, section V
experimental Result with BCVR and GCVR, section VI conclusion.
1.1 Color Features
Fig-1.1: Basic Content Based Video Retrieval Method
Image
Query
Feature
Extraction
Query
Image
Similarity
Measures
Retrieved
Images
Feature
DB
Feature
Extraction
Image
Collection

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Color is one of the most widely used visual features in multimedia context and image
/ video retrieval, To support communication over the Internet, the data should compress well
and be suitable for heterogeneous environment with a variety of the user platforms and
viewing devices, large scatter of the user's machine power, and changing viewing conditions.
The CBVR systems are not aware usually of the difference in original, encoded, and
perceived colors, e.g., differences between the colorimetric and device color data. It is also an
important feature of perception, comparing with other image features such as texture and
shape etc, color features are very stable and robust[2].
It is not sensitive to rotation, translation and scale changes. Moreover, the color
feature calculation is relatively simple. A color histogram is the most used method to extract
color features.
2. HISTORY
Shape-based image/video retrieval is one of the hardest problems in general
image/video retrieval. This is mainly due to the difficulty of segmenting objects of interest in
the images. Consequently, shape retrieval is typically limited to well distinguish objects in
the image. In order to detect and determine the border of an object an image may need to be
preprocessed. The preprocessing algorithm or filter depends on the application. Different
object types such as skin lesions, brain tumors, persons, flowers, and airplanes may require
different algorithms. If the object of interest is known to be darker than the background, then
a simple intensity thresholding scheme may isolate the object. For more complex scenes,
noise removal and transformations invariant to scale and rotation may be needed. Once the
object is detected and located, its boundary can be found by edge detection and boundary
following algorithms. The detection and shape characterization of the objects becomes more
difficult for complex scenes where there are many objects with occlusions and shading. Once
the object border is determined the object shape can be characterized by measures such as
area, eccentricity (e.g., the ratio of major and minor axes), circularity (closeness to a circle of
equal area), shape signature (a sequence of boundary-to-center distance numbers), shape
moments, curvature (a measure of how fast the border contour is turning), fractal dimension
(degree of self similarity) and others. All of these are represented by numerical values and
can be used as keys in a multidimensional index structure to facilitate retrieval.
In dimensionality color histograms may include hundreds of colors. In order to
efficiently compute histogram distances, the dimensionality should be reduced. Transform
methods such as K-L and Discrete Fourier Transform (DFT), Discrete Cosine Transform
(DCT) or various wavelet transforms can be used to reduce the number of significant
dimensions. Another idea is to find significant colors by color region extraction and compare
images based on presence of significant colors only partitioning strategies such as the
Pyramid-Technique map n-dimensional spaces into a one-dimensional space and use a B+
tree to manage the transformed data. The resulting access structure shows significantly better
performance for large number of dimensions compared to methods such as R-tree variants.
Texture and shape retrieval differ from color in that they are defined not for individual
pixels but for windows or regions. Texture segmentation involves determining the regions of
image with homogeneous texture. Once the regions are determined, their bounding boxes
may be used in an access structure like an R-tree. Dimensionality and cross correlation
problems also apply to texture and can be solved by similar methods as in color. Fractal
codes capture the self similarity of texture regions and are used for texture based indexing
and retrieval. Shapes can be characterized by methods and represented by n-dimensional

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numeric vectors which become points in n-dimensional space. Another approach is to
approximate the shapes by well defined, simpler shapes. For instance, triangulation or a
rectangular block cover can be used to represent an irregular shape. In this latter scheme, the
shape is approximated by collection of triangles or rectangles, whose dimensions and
locations are recorded. The advantage of this approach is that storage requirements are lower,
comparison is simpler and the original shape can be recovered with small error [3].
2.1 Color based video retrieval using DCT
The DCT block computes the unitary discrete cosine transform (DCT) of each
channel in the M-by-N input matrix, u. y = dct(u) % Equivalent MATLAB code When the
input is a sample-based row vector, the DCT block computes the discrete cosine transform
across the vector dimension of the input. For all other N-D input arrays, the block computes
the DCT across the first dimension. The size of the first dimension (frame size), must be a
power of two. To work with other frame sizes, use the Pad block to pad or truncate the frame
size to a power-of-two length. When the input to the DCT block is an M-by-N matrix, the
block treats each input column as an independent channel containing M consecutive samples.
The block outputs on-by-N matrix whose lth column contains the length-M DCT of the
corresponding input column [4].
Fig 2.1: Comparison of DCT with row and column
3. METHODS
Content-Based video search is according to the user-supplied in the bottom
Characteristics, directly find out images containing specific content from the image library
the basic process, First of all, do appropriate pre-processing of images like size and image
transformation and noise reduction is taking place, and then extract image characteristics
needed from the image according to the contents of videos to keep in the database. When we
retrieve to identify the video, extract the corresponding features from a known image and
then retrieve the video database to identify the images which are similar with it, also we can
give some of the characteristics based on a query requirement, then retrieve out the required
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
DCT ROW COLOUMN
Series1

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videos based on the given suitable values. In the whole retrieval process, feature extraction is
essential it is closely related to all aspects of the feature, such as color, shape, texture and
space.
Feature extraction based on the color characteristics in the proposed method is
classified into two types.
3.1 Global color based method
Because an RGB color space does not meet the visual requirements of people, for
video retrieval, the video normally converts from an RGB space to other color spaces. The
HSV space is used as it is a more common color space.
The global color is a simple way of extracting image features. High effective
calculation and matching are its main advantage. The feature is invariable to rotation and
translation.
The drawback is that the global color only calculates the frequency of colors. The
spatial distribution of color information is lost. Two completely different images can get the
same global color difference graph, which will cause retrieval errors.
3.2 Block color based method
For the block color, an image is separated into n×n blocks. Each block will have less
meaning if the block is too large, while computation of retrieval process will be increased if
the block is too small. A two-dimensional space divided into 3 × 3 will be more effective. For
each block, we carry out a calculation of the color space converting and color quantization.
The normalized color features for each block can be calculated. Usually in order to highlight
the specific weight of different blocks, a weight coefficient is distributed to each block, and
the weight of middle block is often larger [5].
3.3 Similarity of Frame sequence
Video access is one of the important design issues in the development of multimedia
information system, video-on-demand and digital library. Video can be accessed by attributes
of traditional database techniques, by semantic descriptions of traditional information
retrieval technique, by visual features and by browsing. Access by attributes of database
technique and access by semantic descriptions of information retrieval technique are
insufficient for video access owing to the numerous interpretations of visual data. Besides,
the automatic extraction of semantic information from general video programs is outside the
capability of the current technologies of machine vision.
3.3.1 Symmetric similarity measures
A similarity measure is symmetric if D (U, V) = D (V, U). The straightforward
measure of similarity is the one to- one optimal mapping. In the one to one mapping, we try
to map as many pairs of frames as possible under the constraint that each frame ui in U
corresponds to only one frame vj in V. Obviously, the maximal number of mapping pairs is
equal to the number of frames of shorter shot (shot with less number of frames). The
mapping with minimum sum of frame distance is selected as the optimal mapping. The
formal definitions are given as follows. The effect of video segmentation also affects the
performance of sequence mapping. Video segmentation with lower threshold produces shots
with much variation. It is possible that the shot U is very similar to the V except that some
frames are very dissimilar. Using OM, these dissimilar frame pairs produce large distance.
Therefore, in the next definition, the mapping is constrained by a threshold. Two frames with

6
distance larger than are not allowed to match. In addition, each unmatched frame is
associated with a penalty value v in the computation of shot distance. Otherwise, the result of
the distance-constrained optimal mapping with minimum cost would be no matching at all.
No matching produces the distance of zero.
3.3.2 Asymmetric similarity measures
A similarity measure is asymmetric if D (U, V) ¹ D (V, U). In general, asymmetric
similarity measure is used to map between the query sequence of frames and video sequence
of frames. The simplest proposed asymmetric similarity measure is the Optimal Subsequence
Mapping (OSM). The algorithm of OSM is similar to that of OM except that the query video
sequence must be the shorter sequence. Therefore, it is not necessary to compare the length
between the video shot U and query video shot Q [6].
3.4 Find matching videos
Here absolute difference (AD) is used for similarity measurement of feature vectors of
the videos in content based video retrieval. In this method, the feature vectors of a query
video are compared with feature vectors of all the videos stored in the database by taking
absolute difference between them. The differences will be ordered in increasing fashion so as
to show most relevant searches on top, as the videos with which the absolute difference will
be lower will be most relevant ones[4].
3.5 Calculation of Distance metrics
In the image database and video database proposed method compares with the images
given in query and compare with video database features as with each frames from the video
.for the each frames method calculates Euclidean distance takes the mean of the distances
calculated and then it is stored into the feature vector.
4. IMPLEMENTATION
For the purpose of implementation MATLAB 2012 is used. MATLAB is a "Matrix
Laboratory", and as such it provides many convenient ways for creating vectors, matrices,
and multi-dimensional arrays. In the MATLAB vernacular, a vector refers to a one
dimensional (1×N or N×1) matrix, commonly referred to as an array in other programming
languages. A matrix generally refers to a 2-dimensional array, i.e. an m x n array where m
and n are greater than or equal to 1. Arrays with more than two dimensions are referred to as
multidimensional arrays. Operating system windows 7 is used for the proposed method.
The Software Requirement: Matlab (2012a), The Hardware Requirement: The RAM:
2 GB with Operating System Windows 7/XP.
The first step for video retrieval based on the partitioning of a video sequence into
shots. A shot is defined as an image sequence. Key-frames are images extracted from original
video data. Key-frames have been frequently used to supplement the text of a video log,
though they were selected manually in the past .Key-frames, if extracted properly, are a very
effective visual abstract of video contents and are very useful for fast video browsing. key-
frames can also be used in representing video in retrieval video index may be constructed
based on visual features of key-frames, and queries may be directed at key-frames using
query by retrieval algorithms.
Next step is to extract features. The features are typically extracted with the above
mentioned methods i.e. For color feature extractions methods used is global and block color

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based system. Low-level features such as object motion, color, shape, texture, loudness,
power spectrum, bandwidth, and pitch are extracted directly from video in the database.
High-level features are also called semantic features. Features such as timbre, rhythm,
instruments.
Next step is to find matching videos with both methods using Euclidean distance
formula. Each video is divided into five frames. For each frame Euclidean distance is
calculated take mean of that Euclidean distance, arrange it into ascending order, we will get
most closed matching videos to the given image.
4.1 Color description
In general, color is one of the most dominant and distinguishable low-level visual
features in describing video image. Many CBIR systems employ color to retrieve images In
theory, it will lead to minimum error by extracting color feature for retrieval using real color
image directly, but the problem is that the computation cost and storage required will expand
rapidly In fact, for a given color video image, the number of actual colors only occupies a
small proportion of the total number of colors in the whole color space. In the HSV color
space, each component occupies a large range of values. If we use direct values of H, S and V
components to represent the color feature, it requires lot of computation. So it is better to
quantify the HSV color space into non-equal intervals. At the same time, because the power
of human eye to distinguish colors is limited, we do not need to calculate all segments.
Unequal interval quantization according the human color perception has been applied on H, S
and V components [7].
4.1.1. Color set notation
Transformation
The color set representation is defined as follows: given the color triple (r; g; b) in the
3-D RGB color space and the transform T between RGB and a generic color space denoted
XY Z, for each (r; g; b) let the triple (x; y; z) represent the transformed color such that (x; y;
z) = T(r; g; b):
Quantization
Let QM be a vector quantizer function that maps a triple (x; y; z) to one of M bins.
Then m, where m is the index of color (x; y; z) assigned by the quantizer function and is
given by m = QM(x; y; z):
Binary Color Space
Let BM be an M dimensional binary space that corresponds to the index produced by
QM such that each index value m corresponds to one axis in BM.
Definition 1: Color Set, A color set is a binary vector in BM. A color set corresponds to a
selection of colors from the quantized color space. For example, let T transform RGB to HSV
and let QM where M = 8 vector quantize the HSV color space to 2 hues, 2 saturations and 2
values. QM assigns a unique index m to each quantized HSV color. Then B8 is the 8-
dimensionalbinary space whereby each axis in B8 corresponds one of the quantized HSV
colors. Then a color set c contains a selection from the 8 colors. If the color set corresponds
to a unit length binary vector then one color is selected. If a color set has more than one non-
zero value then several colors are selected. For example, the color set c = (10010100)

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corresponds to the selection of three colors, m = 7; m = 4 and m = 2, from the quantized HSV
color space.
4.2 Database
Small Training Databases: The videos are categorized in number of frames as each video is
divided into five frames assorted categories containing 8 similar videos. So, in total there are
8 videos in data set for experimentation. All the videos are normalized in each category. The
videos are chosen such that those of same categories differ very minutely in color content so
as to find accurate result.
Test Database: In proposed method test database contains 8 videos .search image is compared
with the video database for color feature extraction. Comparative difference graph is plotted
by Euclidean distance.
Created video database is used for the proposed method.
Large Training Database: the images are considered as large training database. There are 15
search images in the image database. All the images are compared with video database for
color feature extraction. Following image database is used in the proposed method.
Fig 4.2: Sample Image Database
4.3 Video retrieval algorithm is as follows
Step 1: First divide the video into no of frames.
Step 2: Uniformly divide each video in the database and the target image
Step 3: Extract Feature with color based methods for Efficient Video retrieval
Step 4: construct a combined feature vector for color and differential graph

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Step 5: Find the distances between feature vector of query Video and the feature vectors of
target video using Euclidean distance.
Step 6: Sort the Euclidean distances by comparing the query image and database video in
ascending order
Step 7: Retrieve most similar Video with minimum Distance from Video data.
5. EXPERIMENTAL RESULT WITH BCVR AND GCVR
5.1 Comparisons
In the proposed method results are shown with block color based video retrieval and
global color based video r retrieval including calculation of Euclidean distances.
Table 5.1: Search image results
Search image 12 Search image 3 Search image 14
GCVR BCVR GCVR BCVR GCVR BCVR
2.15444e+09 4.13454e+08 5.11385e+09 8.69056e+08 3.30671e+09 6.47014e+08
2.78191e+09 7.09396e+08 6.7226e+09 9.24809e+08 3.96584e+09 7.34128e+08
3.85552e+09 7.26439e+08 6.83174e+09 1.07362e+09 4.64586e+09 8.46765e+08
4.06727e+09 7.39199e+08 7.70484e+09 1.10147e+09 5.36469e+09 8.951e+08
4.86135e+09 7.41091e+09 8.94948e+09 1.11417e+09 6.66659e+09 9.7927e+08
Fig-5.1: Search image comparison
0.00E+00
1.00E+09
2.00E+09
3.00E+09
4.00E+09
5.00E+09
6.00E+09
7.00E+09
8.00E+09
9.00E+09
1.00E+10
GCVR BCVR GCVR BCVR GCVR BCVR
Search image 12 Search image 3 Search image 14
Series1
Series2
Series3
Series4
Series5
Euclidean distance
measurements

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Fig- 5.2: Difference graph
5.2 Accuracy with GCVR and BCVR
Table 5.2: Search image Accuracy percentage
Search Image
Method
GCVR
Accuracy %
BCVR
Accuracy %
Image 1 75 79
Image 2 65 69
Image 3 85 90
Image 4 90 95
Image 5 50 55
Image 6 90 91
Image 7 45 41
Image 8 89 92
Image 9 65 76
Image 10 32 43
Image 11 67 53
Image 12 89 92
Image 13 91 96
Image 14 92 96
Image 15 54 60

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Fig 5.2: Search image accuracy by GCVR and BCVR
6. CONCLUSION
Despite the considerable progress of academic research in video retrieval, there has
been relatively little impact of content based video retrieval research on commercial
applications with some niche exceptions such as video segmentation. Choosing features that
reflect real human interest remains an open issue. One promising approach is to use Meta
learning to automatically select or combine appropriate features. Another possibility is to
develop an interactive user interface based on visually interpreting the data using a selected
measure to assist the selection process. We would be interested to see how these techniques
can be incorporated in an interactive video retrieval system. The usefulness of color
correlation statistics in semantic retrieval of video and images. It shows HSV Color is to be
efficient feature over the other more traditional color-based features. MPEG-7 color structure
descriptor is similar to HSV color correlogram and is very efficient in retrieval of
images/videos. This proposed method covers the following tasks: image search that means
search is based on the images. extraction of features of static key frames using color features
specifically HSV color features using conversion of RGB to HSV, video may be TV video,
natural images, collection of frames, video search including interface, similarity measure,
video retrieval and distance metrices.we made program in two parts database creation,
videos, samples, one program is for video file reading then save into database. Search images
option will search images which are closer to the video. Advanced filtering and retrieval
methods for all types of multimedia data are highly needed. Current image and video retrieval
systems are the results of combining research from various fields. Hence color-based retrieval
has been the backbone for content-based image and video retrieval compared with the other
methods like shape or texture.
6.1 Future Scope
Many issues are still open and deserve further research, especially in the following
areas. Most current video indexing approaches depend heavily on prior domain knowledge.
This limits their extensibility to new domains. The elimination of the dependence on domain
0
20
40
60
80
100
120
Image1
Image2
Image3
Image4
Image5
Image6
Image7
Image8
Image9
Image10
Image11
Image12
Image13
Image14
Image15
GCVR Accuracy %
BCVR Accuracy %

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knowledge is a future research problem. Fast video search using hierarchical indices are all
interesting research questions. Video indexing and retrieval in the cloud computing
environment, where the individual videos to be searched and the dataset of videos are both
changing dynamically, will form a new and flourishing research direction in video retrieval in
the very near future. Video affective semantics describe human psycho-logical feelings such
as romance, pleasure, violence, sadness, and anger.
6.2 Applications
Specially used in supercomputers and mainframe computers, because they have high
computation time. In professional and educational activities that involve generating or using
large volumes of video and multimedia data are prime candidates for taking advantage of
video-content analysis techniques. Automated authoring of web content media organizations
and TV broadcasting companies have shown considerable interest in presenting their
information on the web. Automated authoring of web content searching and browsing large
video archives major news agencies and TV broadcasters own large archives of video that
have been accumulated over many years. Intelligent video segmentation and sampling
techniques can reduce the visual contents of the video program to a small number of static
images. Easy access to educational material the availability of large multimedia libraries that
we can efficiently search has a strong impact on education. Indexing and archiving
multimedia presentation.
Consumer domain applications, video content analysis research are geared toward
large video archives. However, the widest audience for video content analysis is consumers.
We all have video content pouring through broadcast TV and cable. Also, as consumers, we
own unlabeled home video and recorded tapes. To capture the consumer’s perspective, the
management of video information in the home entertainment area will require sophisticated
yet feasible techniques for analyzing, filtering, and browsing video by content [8].
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