A survey on coding binary visual features extracted from video sequences

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 01 | Jan -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1520
A SURVEY ON CODING BINARY VISUAL FEATURES EXTRACTED FROM
VIDEO SEQUENCES
Sreejaya1, Anu Vijayan2 , Athira Krishnan3 , Dhanya Sreedharan4
1 B.Tech Student, Department Of Computer Science and Engineering, Sree Buddha College Of Engineering,
Alappuzha, Kerala, India
4Assistant Professor, Department Of Computer Science and Engineering, Sree Buddha College Of Engineering,
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Abstract - In pattern recognition and in image
processing, feature extraction starts from an initial set of
measured data and builds derived values intended to be
informative and non-redundant, facilitating the subsequent
learning and generalization steps, and in some cases leading
to better human interpretations.When the input data to
an algorithm is too large to be processed and it is suspected
to be redundant then it can be transformed into a reduced
set of features . Visual descriptors are descriptions of the
visual features of the contents in images, videos, or
algorithms or applications that produce such descriptions.
KeyWords: Pattern recognition ,Visual features, Image
processing, Feature extraction,Visual descriptors.
1.INTRODUCTION
Feature extraction is a type of dimensionality
reduction that efficiently represents interesting parts
of an image as a compact feature vector. This
approach is useful when image sizes are large and a
reduced feature representation is required toquickly
complete tasks such as image matching and
retrieval.Descriptors are the first step to find out the
connection between pixels contained in a digital
image and what humans recall after having observed
an image or a group of images after some
minutes.Binary local features represent an
alternative to real-valued descriptors.A compact
representation based on global features is preferred
when dealing with large collections.
The visual content is acquired at a node,
compressed and then sent to a central unit for
further processing according to the compress-then-
analyze (CTA) paradigm in the case of traditional
approach.. In the traditionally adopted compress-
then-analyze (CTA) paradigm, images acquired from
camera nodes are JPEG compressed and sent to a
central controller for further analysis.In the case of
analyze-then-compress (ATC) approach camera
nodes perform visual features extraction.It then
transmit a compressed version of the extracted
features and the relative keypoints information to a
central controller. At the central controller, the
received features are matched against a database of
labeled features, so that object recognition or image
retrieval can be performed .

2 LITERATURE SURVEY
2.1 Coding Binary Local Features Extracted From
Video Sequences
Local features refer to a pattern or distinct structure
found in an image, such as a point, edge, or small
image patch. They are usually associated with an
image patch that differs from its immediate
surroundings by texture, color, or intensity. Feature
extraction involves computing a descriptor, which is
typically done on regions centered around detected
features. Descriptors rely on image processing to
transform a local pixel neighborhood into a compact
vector representation. This new representation
permits comparison between neighborhoods
regardless of changes in scale or orientation.
Descriptors, such as SIFT or SURF, rely on local
gradient computations. Binary descriptors, such as
BRISK or FREAK, rely on pairs of local intensity
differences, which are then encoded into a binary
vector.
2.2 Compress-Then-Analyze VS. Analyze- Then-
Compress
In the case of compress-then-analyze (CTA)
paradigm, images acquired from camera nodes are
JPEG compressed and sent to a central controller for
further analysis.In the case of analyze-then-compress
(ATC) approach camera nodes perform visual
features extraction.It then transmit a compressed
version of the extracted features and the relative
keypoints information to a central controller. At the
central controller, the received features are matched
against a database of labeled features, so that object
recognition or image retrieval can be performed .
2.3 Evaluation Of Low – Complexity Visual
Feature Detectors And Descriptors
Feature detection selects regions of an image that
have unique content, such as corners or blobs. Use
feature detection to find points of interest that you
can use for further processing. These points do not
necessarily correspond to physical structures, such
as the corners of a table. The key to feature detection
is to find features that remain locally invariant so
that you can detect them even in the presence of
rotation or scale change.Binary descriptors can
achieve a performance similar to that of non
binarydescriptors,with much lower
complexity.Evaluation is done for an image retrieval
task as well as standalone.a good score in the
standalone evaluation did not always lead to high
accuracy in the image retrieval task.
2.4 Rate -Accuracy Optimization Of Binary
Descriptors
Scale-invariant feature transform (SIFT) is an
algorithm to detect and describe local features in
images. SIFT can robustly identify objects even
among clutter and under partial occlusion, because
the SIFT feature descriptor is invariant to uniform
scaling, orientation, and partially invariant to affine
distortion and illumination changes.The descriptor is
represented by means of a binary string, in which
each bit is the result of the pair-wise comparison of
smoothed pixel values properly selected in a patch
around each keypoint.

3 CONCLUSION
This paper deals with various coding schemes
tailored to both local and global binary
features,which aims at exploiting both spatial and
temporal redundancy by means of intra- and inter-
frame coding.Binary local features represent an
alternative to real-valued descriptors.The efficiency
was evaluated by means of rate-efficiency curves
with respect to traditional visual analysis tasks. This
survey is basically done to study about various
coding schemes for extracting local and global binary
visual features from video sequences.
ACKNOWLEGEMENT
We are deeply thankful to our guide Ms Dhanya
Sreedharan, Assistant professor of Computer Science
and Engineering, for guiding us through the difficult
phases of our work and inspiring us during each
stage of our work. We express sincere thanks to all
other faculty members for encouraging us in each
stage.
REFERENCES
[1] Coding binary local features extracted from video
sequences - L. Baroffio, J. Ascenso, M. Cesana, A.
Redondi, and M. Tagliasacchi
[2] Compress - then - analyze vs. analyse - then -
compress: Two paradigms for image analysis in
visual sensor networks - A. Redondi, L. Baroffio, M.
Cesana, and M. Tagliasacchi
[3] Evaluation of low - complexity visual feature
detectors and Descriptors - A. Canclini, M. Cesana, A.
Redondi, M. Tagliasacchi, J. Ascenso, and R. Cilla
[4] Rate-accuracy optimization of binary descriptors
- A. Redondi, L. Baroffio, J. Ascenso, M. Cesano, and M.
Tagliasacchi
[5] Video Google: A text retrieval approach to object
matching in videos - J. Sivic and A. Zisserman
[6] Distinctive image features from scale-invariant
keypoints - D. G. Lowe
[7] Aggregating local descriptors into a compact
image representation - H. Jegou, M. Douze, C. Schmid,
and P. Perez
[8] Binary local descriptors based on robust hashing
- L. Baroffio, M. Cesana, A. Redondi, and M.
Tagliasacchi
[9] BRIEF: Binary robust independent elementary
features - M. Calonder, V. Lepetit, C. Strecha, and P.
Fua
[10] BRISK: Binary robust invariant scalable
keypoints - S. Leutenegger, M. Chli, and R. Y. Siegwart

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