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Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.65-67
ISSN: 2278-2419
65
Mining and Clustering the Feature Similarities of
Images on Smart Phone
M.S. Samiya1
, S. Sharmiladevi1
, K. Surya1,
R.Sudha2
B.TECH (IT) Final Year – Arasu Engineering College, Kumbakonam, India.
Assistant Professor –IT, Arasu Engineering College, Kumbakonam, India.
Abstract-With the fame of visual sensor on smart phone
devices, ( i.e. camera) it becomes a habit for many people to
capture photos everyday and everywhere. This led to the rapid
developing of more personal images and becomes a nuisance to
the users in storing and organizing them, which had not been
used before. Luckily, cloud storage provided a
comprehensive solution at the right moment, and it
facilitates the synchronization and sharing of images
acquired. However, organizing this bulk number of personal
images is still a tedious and difficult task. Common needs in
photo organization may involve tagging, destroying replicated
or same images, and collecting photos into albums. In our
proposed system, we target to provide a features similarity
images, face detection and recognition, avoid redundancy on
smart mobile application which makes use of existing sensors
and related technologies to help users to manage replicate or
same images more effectively. By sharpening the power of
cloud computing for SSIM algorithm, our system significantly
reduce the time spent on managing photos in a neat and simple
way which reduce user stress and increase user experience.
Keywords- Photo organization, Image similarity, Cloud
storage, Mobile application, Redundancy avoidance.
I. INTRODUCTION
In our daily life, we are storing bulk number of photos. In this
approach, we face different nuisance on storing and organising
photos. For this problem, we found out a solution that is being
carried out by location analysis, image analysis, face detection
and recognition and also avoid redundancy. By these
techniques, we easily storing and organizing the photos. We
implement all techniques in android platform. It is very
helpful in today world.
II. RELATED WORKS
The structural similarity index (SSIM) is a method for
predicting the perceived quality of digital television and
cinematics pictures, as well as other kinds of digital images
and videos. SSIM is used for measuring the similarity between
the two images. The SSIM index is a full reference metric. In
other words, the measurement or prediction of image quality is
based on an initial uncompressed or distortion-free images
reference.
III. SYSTEM MODULES
Grafter phase
User interface
Operations
Location Analysis
Image Analysis,
Face detection and recognition
IV. GRAFTER PHASE
User capture the photos and those photos are inserted into
portfolio. Grafter upload the images into repository for future
retrieval.
V. USER INTERFACE
User can easily choose searching the images in a number of
ways from the top menu bar. From the drop-down menu ,users
can select to search by location , image and face.
Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.65-67
ISSN: 2278-2419
66
VI. OPERATION
User can select any of the choices from the menu bar. Based on
their choices images can be retrieved from the repository and
flourish to user.
VII.LOCATION ANALYSIS
Photos captured on mobile devices are likely to contain
geographical tagging information. With the use of SSIM
algorithm, photos acquired at the same place or near locations
will be grouped in a cluster.
VIII.IMAGE ANALYSIS
User can select the image analysis from menu and retrieve the
images from repository. By using, SSIM algorithm flourish the
similar group photos to user.
IX. FACE DETECTION AND RECOGNITION:
User can select the face detection and recognition choice and
retrieve the images from repository. By using, SSIM algorithm
flourish the similar photos to user.
Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications
Volume 5, Issue 1, June 2016, Page No.65-67
ISSN: 2278-2419
67
X. CONCLUSION
We have demonstrate the use of existing technologies in
organizing the daily expending photo collection. By
introducing various sorting and searching capabilities, users
can reorganize or seek their photos based on the needs. This
relieves the tedious and painful manual album creation. Our
current prototype focusing on the use of geographical analysis,
image similarity analysis and face recognition to facilitate
these processes. By leveraging the computation power of
cloud, our system enables the use of mobile devices alone in
managing user’s own photo collection. In future, we are going
to investigate the possibility of machine learning to provide
customized and personalized photo organizing features.
Reference
[1] B. Fernando and T. Tuytelaars, “Mining multiple queries
for image retrieval: On-the-fly learning of an object-
specific mid-level represen- tation,” in Proc. IEEE ICCV,
Dec. 2013, pp. 2544–2551.
[2] S. Zhang, M. Yang, T. Cour, K. Yu, and D. N. Metaxas,
“Query specific rank fusion for image retrieval,” IEEE
Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 803–
815, Apr. 2015.
[3] Z. Wu, Q. Ke, M. Isard, and J. Sun, “Bundling features for
large scale partial-duplicate web image search,” in Proc.
IEEE Conf. CVPR, Jun. 2009, pp. 25–32.
[4] Xiaoou Tang, Fellow,Ke Liu, Jingyu Cui, Student
Member,Fang Wen, Member and Xiaogang Wang,
Member, “IntentSearch: Capturing User Intention for One-
Click Internet Image Search”, IEEE transactions on
pattern analysis and machine intelligence, vol. 34, no. 7,
july 2012.
[5] Zhuo YANG , Sei-ichiro KAMATA,” NIR: Content Based
Image Retrieval on Cloud Computing”, IEEE transactions
on pattern analysis and machine intelligence, 2009
[6] Reena Rachagiri, D. Baswaraj, Dr. M Janga Reddy,”
Approaches for User Image Search Goals Using Grouping
Similarity”, International Journal of Engineering and
Innovative Technology (IJEIT) Volume 5, Issue 1, July
2015
[7] Yi Wang, Wenjie Hu, Yibo Wu and Guohong Cao,
SmartPhoto: A Resource-Aware Crowdsourcing Approach
for Image Sensing with Smartphones,PA, 16802, USA.
[8] Reena Rachagiri, D. Baswaraj, Dr. M Janga Reddy
,Approaches for User Image Search Goals Using Grouping
Similarity International Journal of Engineering and
Innovative Technology (IJEIT) Volume 5, Issue 1, July
2015
[9] Q. Luo, S. Zhang, T. Huang, W. Gao, and Q. Tian,
“Scalable mobile search with binary phrase,” in Proc. 5th
ICIMCS, 2013, pp. 66–70.
[10]X. Wang, M. Yang, T. Cour, S. Zhu, K. Yu, and T. X.
Han, “Contextual weighting for vocabulary tree based
image retrieval,” in Proc. IEEE ICCV, Nov. 2011, pp.
209–216
[11]Q. Luo, S. Zhang, T. Huang, W. Gao, and Q. Tian,
“Scalable mobile search with binary phrase,” in Proc. 5th
ICIMCS, 2013, pp. 66–70.
Profile
Mrs.R.SUDHA M.E working as Assistant Professor in Arasu
Engineering College approved by AICTE and Anna University
Chennai. She was experience in Cloud Computing, Mobile
Computing, Data Mining.
Miss.M.S. SAMIYA is a student of B.TECH (IT) at Arasu
Engineering College approved by AICTE and Anna University
Chennai. Her areas of interest are Data Mining And Web
Designing.
Miss.S. SHARMILADEVI is a student of B.TECH (IT) at
Arasu Engineering College approved by AICTE and Anna
University Chennai. Her area of interest are Data Mining.
Miss.K. SURYA is a student of B.TECH (IT) at Arasu
Engineering College approved by AICTE and Anna University
Chennai. Her areas of interest are Database Management
System and Data Mining.

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Mining and Clustering the Feature Similarities of Images on Smart Phone

  • 1. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications Volume 5, Issue 1, June 2016, Page No.65-67 ISSN: 2278-2419 65 Mining and Clustering the Feature Similarities of Images on Smart Phone M.S. Samiya1 , S. Sharmiladevi1 , K. Surya1, R.Sudha2 B.TECH (IT) Final Year – Arasu Engineering College, Kumbakonam, India. Assistant Professor –IT, Arasu Engineering College, Kumbakonam, India. Abstract-With the fame of visual sensor on smart phone devices, ( i.e. camera) it becomes a habit for many people to capture photos everyday and everywhere. This led to the rapid developing of more personal images and becomes a nuisance to the users in storing and organizing them, which had not been used before. Luckily, cloud storage provided a comprehensive solution at the right moment, and it facilitates the synchronization and sharing of images acquired. However, organizing this bulk number of personal images is still a tedious and difficult task. Common needs in photo organization may involve tagging, destroying replicated or same images, and collecting photos into albums. In our proposed system, we target to provide a features similarity images, face detection and recognition, avoid redundancy on smart mobile application which makes use of existing sensors and related technologies to help users to manage replicate or same images more effectively. By sharpening the power of cloud computing for SSIM algorithm, our system significantly reduce the time spent on managing photos in a neat and simple way which reduce user stress and increase user experience. Keywords- Photo organization, Image similarity, Cloud storage, Mobile application, Redundancy avoidance. I. INTRODUCTION In our daily life, we are storing bulk number of photos. In this approach, we face different nuisance on storing and organising photos. For this problem, we found out a solution that is being carried out by location analysis, image analysis, face detection and recognition and also avoid redundancy. By these techniques, we easily storing and organizing the photos. We implement all techniques in android platform. It is very helpful in today world. II. RELATED WORKS The structural similarity index (SSIM) is a method for predicting the perceived quality of digital television and cinematics pictures, as well as other kinds of digital images and videos. SSIM is used for measuring the similarity between the two images. The SSIM index is a full reference metric. In other words, the measurement or prediction of image quality is based on an initial uncompressed or distortion-free images reference. III. SYSTEM MODULES Grafter phase User interface Operations Location Analysis Image Analysis, Face detection and recognition IV. GRAFTER PHASE User capture the photos and those photos are inserted into portfolio. Grafter upload the images into repository for future retrieval. V. USER INTERFACE User can easily choose searching the images in a number of ways from the top menu bar. From the drop-down menu ,users can select to search by location , image and face.
  • 2. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications Volume 5, Issue 1, June 2016, Page No.65-67 ISSN: 2278-2419 66 VI. OPERATION User can select any of the choices from the menu bar. Based on their choices images can be retrieved from the repository and flourish to user. VII.LOCATION ANALYSIS Photos captured on mobile devices are likely to contain geographical tagging information. With the use of SSIM algorithm, photos acquired at the same place or near locations will be grouped in a cluster. VIII.IMAGE ANALYSIS User can select the image analysis from menu and retrieve the images from repository. By using, SSIM algorithm flourish the similar group photos to user. IX. FACE DETECTION AND RECOGNITION: User can select the face detection and recognition choice and retrieve the images from repository. By using, SSIM algorithm flourish the similar photos to user.
  • 3. Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications Volume 5, Issue 1, June 2016, Page No.65-67 ISSN: 2278-2419 67 X. CONCLUSION We have demonstrate the use of existing technologies in organizing the daily expending photo collection. By introducing various sorting and searching capabilities, users can reorganize or seek their photos based on the needs. This relieves the tedious and painful manual album creation. Our current prototype focusing on the use of geographical analysis, image similarity analysis and face recognition to facilitate these processes. By leveraging the computation power of cloud, our system enables the use of mobile devices alone in managing user’s own photo collection. In future, we are going to investigate the possibility of machine learning to provide customized and personalized photo organizing features. Reference [1] B. Fernando and T. Tuytelaars, “Mining multiple queries for image retrieval: On-the-fly learning of an object- specific mid-level represen- tation,” in Proc. IEEE ICCV, Dec. 2013, pp. 2544–2551. [2] S. Zhang, M. Yang, T. Cour, K. Yu, and D. N. Metaxas, “Query specific rank fusion for image retrieval,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 803– 815, Apr. 2015. [3] Z. Wu, Q. Ke, M. Isard, and J. Sun, “Bundling features for large scale partial-duplicate web image search,” in Proc. IEEE Conf. CVPR, Jun. 2009, pp. 25–32. [4] Xiaoou Tang, Fellow,Ke Liu, Jingyu Cui, Student Member,Fang Wen, Member and Xiaogang Wang, Member, “IntentSearch: Capturing User Intention for One- Click Internet Image Search”, IEEE transactions on pattern analysis and machine intelligence, vol. 34, no. 7, july 2012. [5] Zhuo YANG , Sei-ichiro KAMATA,” NIR: Content Based Image Retrieval on Cloud Computing”, IEEE transactions on pattern analysis and machine intelligence, 2009 [6] Reena Rachagiri, D. Baswaraj, Dr. M Janga Reddy,” Approaches for User Image Search Goals Using Grouping Similarity”, International Journal of Engineering and Innovative Technology (IJEIT) Volume 5, Issue 1, July 2015 [7] Yi Wang, Wenjie Hu, Yibo Wu and Guohong Cao, SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones,PA, 16802, USA. [8] Reena Rachagiri, D. Baswaraj, Dr. M Janga Reddy ,Approaches for User Image Search Goals Using Grouping Similarity International Journal of Engineering and Innovative Technology (IJEIT) Volume 5, Issue 1, July 2015 [9] Q. Luo, S. Zhang, T. Huang, W. Gao, and Q. Tian, “Scalable mobile search with binary phrase,” in Proc. 5th ICIMCS, 2013, pp. 66–70. [10]X. Wang, M. Yang, T. Cour, S. Zhu, K. Yu, and T. X. Han, “Contextual weighting for vocabulary tree based image retrieval,” in Proc. IEEE ICCV, Nov. 2011, pp. 209–216 [11]Q. Luo, S. Zhang, T. Huang, W. Gao, and Q. Tian, “Scalable mobile search with binary phrase,” in Proc. 5th ICIMCS, 2013, pp. 66–70. Profile Mrs.R.SUDHA M.E working as Assistant Professor in Arasu Engineering College approved by AICTE and Anna University Chennai. She was experience in Cloud Computing, Mobile Computing, Data Mining. Miss.M.S. SAMIYA is a student of B.TECH (IT) at Arasu Engineering College approved by AICTE and Anna University Chennai. Her areas of interest are Data Mining And Web Designing. Miss.S. SHARMILADEVI is a student of B.TECH (IT) at Arasu Engineering College approved by AICTE and Anna University Chennai. Her area of interest are Data Mining. Miss.K. SURYA is a student of B.TECH (IT) at Arasu Engineering College approved by AICTE and Anna University Chennai. Her areas of interest are Database Management System and Data Mining.