EMR: A Scalable Graph-based Ranking Model for Content-based Image Retrieval

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EMR: A Scalable Graph-based Ranking Model for Content-based Image Retrieval | IEEE Projects 2015 | Final year projects | BE Projects | ME Projects | Abstract : Graph-based ranking models have been widely applied in information retrieval area. In this paper, we focus on a well known graph-based model – the Ranking on Data Manifold model, or Manifold Ranking (MR). Particularly, it has been successfully applied to content-based image retrieval, because of its outstanding ability to discover underlying geometrical structure of the given image database. However, manifold ranking is computationally very expensive, which significantly limits its applicability to large databases especially for the cases that the queries are out of the database (new samples). We propose a novel scalable graph-based ranking model called Efficient Manifold Ranking (EMR), trying to address the shortcomings of MR from two main perspectives: scalable graph construction and efficient ranking computation. Specifically, we build an anchor graph on the database instead of a traditional k-nearest neighbor graph, and design a new form of adjacency matrix utilized to speed up the ranking. An approximate method is adopted for efficient out-of-sample retrieval. Experimental results on some large scale image databases demonstrate that EMR is a promising method for real world retrieval applications.

EMR: A Scalable Graph-based Ranking Model for Content-based Image Retrieval

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1). BIBLIOGRAPHY
2). CONCLUSION
3). HARDWARE SOFTWARE SPECIFICATION
4). IMPLEMENTATION
5). INPUT DESIGN &OUTPUT DESIGN
6). INTRODUCTION
7). LITERATURE SURVEY
8). SCREENSHOT
9). SOFTWARE ENVIRONMENT
10). SYSTEM ANALYSIS
11). SYSTEM DESIGN
12). SYSTEM STUDY
13). SYSTEM TESTING

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EMR: A Scalable Graph-based Ranking Model for Content-based Image Retrieval

EMR: A Scalable Graph-based Ranking Model for Content-based Image Retrieval

EMR: A Scalable Graph-based Ranking Model for Content-based Image Retrieval

Technology: DOT NET and DOT NET IEEE PROJECTS.Project Tags: BE Projects, Final Year Projects, IEEE Projects 2015, KNOWLEDGE AND DATA ENGINEERING-DATA MINING, and ME Projects.

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