Data Mining and Knowledge Engineering

S. Bharathi ¹, M. Jayapriya ¹, N. Padmaja ¹, R. Ezhilarasi ¹

Affiliations
1 Department of Computer Science and Engineering, VRS College of Engineering and Technology, Arasur, IN

Abstract

With the approach of distributed computing, information managers are persuaded to outsource their complex information administration frameworks from nearby destinations to business open cloud for awesome adaptability and financial funds. Anyway for securing information security, touchy information must be encoded before outsourcing, which obsoletes conventional information use taking into account plaintext magic word seeks. In this manner, empowering an encoded cloud information pursuit administration is of foremost significance. Considering the vast number of information clients and records in cloud, it is essential for the pursuit administration to permit multi-catchphrase question and give result likeness positioning to meet the viable information recovery need. Related deals with searchable encryption concentrate on single catchphrase pursuit or Boolean essential word inquiry, and infrequently separate the query items. In this paper, shockingly, we characterize and fathom the testing issue of security safeguarding multi-essential word positioned hunt over encoded cloud information (MRSE), and build a set of strict security prerequisites for such a protected cloud information usage framework to turn into a reality. Among different multi-essential word semantics, we pick the productive standard of "direction coordinating", i.e., whatever number matches as would be prudent, to catch the similitude between hunt inquiry and information reports, and further utilize "internal item similitude" to quantitatively formalize such standard for comparative estimation. We first propose a fundamental MRSE plan utilizing secure inward item calculation, and afterward fundamentally move forward it to meet diverse protection necessities in two levels of risk.

Keywords

Privacy-Preserving, Multi-Keyword, Coordinate-Matching.

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S. Bharathi ¹, P. Deepa Shenoy ², K. R. Venugopal ³, L. M. Patnaik ⁴

Affiliations
1 Department of MCA, Dr Ambedkar Institute of Technology, Bangalore, IN
2 Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore, IN
3 University Visvesvaraya College of Engineering, Bangalore University, Bangalore, IN
4 Indian Institute of Science, Bangalore, IN

Abstract

The task of indentifying similar objects within the querying image remains challenging. It is due to viewpoint and lighting changes, deformation and partial occlusions that may exist across different examples. In this framework we focus on combination methods that ensemble multiple descriptors at multiple spatial resolution levels. Ensemble PHOG (Pyramid histogram orientation and gradient) and SIFT (Scale invariant feature transformation) descriptors are used for the feature extraction to achieve the good classification accuracy. Within a region local feature was captured by the distribution over edge orientation, and spatial layout by tiling the image into regions at multiple resolutions. The SIFT features are extracted for each PHOG block. These features are trained using SOM network. Later SVM and Neural network classifiers are used for classification. Results demonstrating the effectiveness of the proposed technique are provided using confusion matrix, transition matrix and other accuracy measures. Area of different land cover regions are calculated, which can be used for land use changes.

Keywords

PHOG, SIFT, Classification, Satellite Image.

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