moidex-the mobile tourist guide

8/7/2019 MoidEx-The Mobile Tourist Guide

1/31

Chinmay MohapatraENTC - B, 7th Semester

Regn - 0601212451


2/31


3/31


4/31

CONTENTS

Introduction

Moides Framework

MoidEx Architecture MoidEx Location Detection

Visual Feature Extraction

mTourism Building Recognition System

Mobile Configuration Requirements

Performance Evaluation

Conclusion


5/31

INTRODUCTION

According to World Tourism Org.

In 2008, there were over 922

million international touristarrivals, with a growth of 1.9% as

compared to 2007.

International tourism receipts

grew to US $ 944 billion in 2008,

corresponding to an increase inreal terms of 1.8% on 2007.


6/31

Moides-Mobile Image Description

MoidEx: The mTourism System

The Moides gives the general framework for handset-

based systems in order to extract features from images.


7/31

MoidEx Architecture

TAJ.MPEG

TEXT

MPEG-7

CBIR

Mpeg7 reference

database

CLC


8/31

MPEG-7 is an ISO/IEC standard developed by

Moving Pictures Experts Group (MPEG).

MPEG-7 helps in describing multimediacontent in a standardized way and thus

enables easier archiving, accessing, locating,

navigating, searching, and managing of

audiovisual information.

MPEG-7


9/31

Location Detection-CELL ID

GSM CELL

Mobile Network Code

(MNC)

Mobile Country Code

(MCC)

Cell ID (CID)

Location Area Code

(LAC)


10/31

Location Detection-CELL ID

URBAN

RURAL AREA

RANGE ADVANTAGES

1. High availability.

2. Low cost.

3. Doesnt require any

modification in handset of

cellular network.


11/31

MoidEx uses Cell ID approach.

The general Idea is to determine the current

cell and using its centre as the approximated

location as its handset.

MoidEx Location Detection


12/31

< Cr e a t i o n L o c a t i o n>

IMEI = 358361007597812 ,

MCC = 262 ,

MNC = 01 ,

LAC = 33028 ,

CID = 63857 ,

SIGNAL = 94 ,

BAR = 3

Eg. of a creation location descriptor, created outside a street in London.

MoidEx Location Detection


13/31

VISUAL FEATUREEXTRACTION

CLC ALGORITHM

A Consistent Line Cluster (CLC) is a set of lines that

are homogeneous in terms of some line features.

Color-CLC: The lines have the same color feature.

Orientation-CLC: The lines are parallel to each other.

Spatially-CLC: The lines are in close proximity to each

other.


14/31


15/31

To reduce the complexity, classify each pixel of the image as

one of several dominant colors.

Color feature of lines: Color Pair (c1,c2).


16/31

(contd.)

Lines are clustered according to their color pair(s).


17/31

The lines in an Orientation-CLC are parallel to each other in

the 3D world; the parallel lines of an object in a 2D image can

be:

Still parallel.

Converging to a vanishing point.


18/31

(contd.)

Two steps:

Construct roughly-orientation-CLC;

Filter line clusters using perspective information.


19/31

Vertical position clustering

Horizontal position clustering


20/31

Two types ofbuildings Two criteria:

Inter-relationship criterion


21/31

(contd.)

Intra-relationship criterion


22/31

MAPPING CLC INTO MPEG-7

Given a consistent line cluster cli with lines:

l1= ((x11, y11)(x12, y12)), , ln = ((xn1, yn1)(xn2, yn2))

the proposed mapping of this cluster to the MPEG-7 RegionLocatorType

descriptor is shown in Listening 2.

1 2 3 4 x11 y11 x12 y125 . . .

6 xn1 yn1 xn2 yn27 8 9

Mapping of line clusters to polygons


23/31

MAPPING CLC INTO MPEG-7

Moides

CLC

Algorithm

SELECT

Moides

CANCEL

ProcessingProcessing

Taj.mpeg

BACK

1 2 3


24/31

mTourism Building Recognition System

The mTourism building recognition system has

been developed as application library on topof MPEG-7 MMDB system.

The MPEG-7 MMDB system provides means

for storing, indexing, querying MPEG-7

instance documents.


25/31

KNN-retrieval for mTourism Lib within MPEG-7 MMDB


26/31

Mobile Configuration Requirements

1. Camera Phone (at least 2 megapixel).

2. Connectivity: Bluetooth.

GPRS.

3. Java Micro Edition (J2ME).


27/31

Performance EvaluationPerformance Evaluation

97 well-patterned buildings : 97/97

44 not-well-patterned buildings : 42/44

16 non-patterned non-buildings : 15/16

25 patterned non-buildings : 0/25


28/31


29/31

Conclusion

MoidEx enhanced the original framework by

the means of additional visual features

location detection and an algorithm targeting

on building recognition.

The evaluation showed a recognition rate up

to 90% by combining location and visual

features.

The average error was 5.8%.


30/31

REFERENCES

MoidEx: Location-based mTourism system on mobile devices.M.Dller, G. Kckerandl, S. Jans, L.Limam. DansInternational Conference on Multimedia Computing andSystems, ICMCS'2009, IEEE ed. Ouarzazate, Maroc. 2009.

Moides - Mobile Image Description

Mario Doller and Harald Kosch and Josef Hammer jun.Proceedings of I-KNOW 06 Graz, Austria, September 6 - 8, 2006 .

Consistent line clusters for building recognition in CBIR.

Yi Li and Linda G. Shapiro. In Proceedings of the 16thInternational Conference on Pattern Recognition (ICPR),pages 952956, Washington, DC, USA, 2002. IEEEComputer Society.


31/31

moidex-the mobile tourist guide

Documents