Fast Query Point Movement Techniques

for Large CBIR Systems

TARGETJ SOLUTIONS

REAL TIME PROJECTS IEEE BASED PROJECTSEMBEDDED SYSTEMSPAPER PUBLICATIONSMATLAB PROJECTStargetjsolutions@gmail.com(0)9611582234, (0)9945657526

Contents

Introduction

Scope

Architecture

Class diagram

Sequence diagrams

Use case diagrams

Modules

Purpose

IntroductionTarget search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph.

Existing techniques, designed around query refinement based on relevance feedback (RF), suffer from slow convergence, and do not guarantee to find intended targets.

To address these limitations, we propose several efficient query point movement methods.

We prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases.

We propose a new index structure and query processing technique to improve retrieval effectiveness and efficiency.

We also consider strategies to minimize the effects of users’ inaccurate RF.

Purpose

The main purpose of this document is to meet the requirements as mentioned in the SRS.

Develop a CBIR system that focuses on target search techniques, and faster than the existing CBIR system and which is not a victim to slow convergence, local maximum traps, minimizing the resource requirements.

CBIR system that can handle inefficient relevance feedback (RF).

The user is provided with a flexible user interface in which he/she has to login to the system to use the software.

After login process, the user presents the image of similarity to search, by browsing the local computer.

The users’ query is processed and a list of relevant images are produced.

Purpose

The user picks the images as positive and negative and the positive images are considered for next round of retreival.

Scope

filtering and law enforcement markets .

Crime detection

Cencoring

Some benefits

1. User Feedback is included.

2. Reduces the unrelated searches.

3. The software is sensitive to inaccurate feedback.

4. Future retrievals of images can be processed faster.

Scope

5. Guarantees that the image is found.

6. Can reach target image with fewer iterations.

7. The scenario of local maximum traps and slow convergence is totally eradicated.

8. The images are searched using image properties.

9. The system is not sensitive to users’ inaccurate relevance feedback.

Architecture

Architecture

Architecture

Algorithm1) Naïve random scan (NRS) methodI

The NRS method randomly retrieves k different images at a time until the user finds the target image or the remaining set is exhausted.

At each iteration, a set of k random images are retrieved from the candidate (i.e.unchecked) set S’ for relevance feedback , and S’ is then reduced by k .

In the best case, NRS takes one iteration Ω (1).

while the worst case requires S/K iterations.

At each iteration, a set of k random images are retrieved from the candidate (i.e.unchecked) set S’ for relevance feedback , and S’ is then reduced by k .

In the best case, NRS takes one iteration Ω (1).

while the worst case requires S/K iterations.

Algorithm

2)Local neighboring movement (LNM) methodLNM is similar to NRS except for steps 5 step 6 which is explained as follows:

step5: Qr ←<nQ,PQ,WQ,DQ, S’,k> based on the user’srelevance feedback.step6: Sk ← EVALUATEQUERY(Qr) /* perform a constrained k-NN query */

Qr is constructed such that it moves towards neighboring relevant points and away from irrelevant ones, and a query is now evaluated against S’ instead of S.

One iteration is required in the best case Ω(1).

The worst case O(1) is given by

the average case o(1) is given by .

Algorithm

3) Neighboring divide and conquer (NDS) method

Voronoi diagrams in NDC to reduce search space.

The Voronoi diagram approach finds the nearest neighbors of a given query point by locating the Voronoi cell containing the query point.

NDC searches for the target as follows, from the starting query Qs, k points are randomly retrieved.

Then the Voronoi region VRi is initially set to the minimum bounding box of S.

Algorithm

Instead of using a query point and its neighboring points to construct a Voronoi diagram, GDC uses the query point and k points randomly sampled from V Ri.

Modules

1. Preprocessing (admin)

2. Target search methods Search without virtual process

ModulesSearch with virtual process

Modules

3) Relevance feedback

Modules

4) Virtual feature creation

5) Virtual feature updation

Class diagram

USER

Class diagram

Search I m age

+ Image

+ Upload Image()+ Search()+ Give Feedback()

upload I m age

+ Image

+ upload()+ claculate Histogram()

Search

+ Databsae image+ Buffer image

+ clac hostogram()+ Calculate Distance()+ compare Image()+ virtualprocess()+ displayResult()

FeedBack

+ result

+ Give Result()+ createVirtualFeature()+ updateVirtualFeature()+ uploadDatabase()

User

+ details

+ Login()+ Registration()

User Login

+ UserName+ Password

+ Login()

Regist at ion

+ Detail

+ registation()

ADMIN

Sequence diagrams

ADMIN

Sequence diagrams

USER

Sequence diagrams

VIRTUAL FEATURE

Sequence diagrams

VIRTUAL FEATURE

Use-case Admin

Adm in

Login

ViewList

ViewImage

UploadImage

Use-case User

User

UserLogin

SearchImage

ViewResult

GiveFeedback

Regist r at ion

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