Abstract

In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images ar e transformed intoset of features. These features are used as inputs in ourproposedQuantization algorithm for generating the code word to form visual dictionary. These codewords are used to represent images s emantically to form visual labels using Bag-of-Features (BoF). The Histogram intersection method is used to measure thedistance between input image and the set of images in the image database to retrieve similar images. The experimental results are evaluated over a collection of1000generic Web images to demonstrate the effectiveness of the proposed system.

Highlights

  • Due to rapid changes in digital technologies, in the recent years many people wish to publish their digital information on the Web such as text, image, video, audio etc

  • In the past few years, many researchers have been involved in the area of Content-Based Image Retrieval (CBIR) system to develop techniques to retrieve unannotated images [1]

  • We present a brief description of the datasets used in our study, the feature extraction, quantization and distance measures, Bag-of-Features and image representations, and image retrieval using Bag-of-Words

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Summary

INTRODUCTION

Due to rapid changes in digital technologies, in the recent years many people wish to publish their digital information on the Web such as text, image, video, audio etc. The derived image features are used to retrieve relevant images semantically from the Web. In the past few years, many researchers have been involved in the area of Content-Based Image Retrieval (CBIR) system to develop techniques to retrieve unannotated images [1]. Today many people use a digital images and video libraries as the main source of visual information It is an open challenge for the research community to develop cost effective technologies for retrieving, managing and browsing the images in the Web. Many CBIR systems have been proposed in recent years. The basic idea of this work is that a set of local image blocks is sampled and a vector of visual descriptors is evaluated on each independently by using Dense Scale Invariant Feature Transform (DSIFT).

THE PROPOSED METHODOLOGY
Global Color Model
Quantization and Distance Measures
Image Representations with a bag of visual words
Image Retrieval Using Bag-of-Words
EXPERIMENTAL RESULTS
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