Content Based Image Retrieval (CBIR)

Aman Patel

A211

Under the guidance of : Dr. Dhirendra Mishra

Contents What is CBIR and its need

Work Done (1st Sem)

Action Planned (for 2nd Sem)

CBIR implementation

Architecture of CBIR

Algorithmic approach

Sectorization for DFT

Sectorization Representation

Applications

CBIR and its need Content-based image retrieval (CBIR), also known as query by image

content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases.

CBIR is desirable because most web-based image search engines rely purely on metadata and this produces a lot of garbage in the results.

Also having humans manually enter keywords for images in a large database can be inefficient, expensive and may not capture every keyword that describes the image.

Thus a system that can filter images based on their content would provide better indexing and return more accurate results.

Work Done (1st Sem) CBIR papers study and summarization.

Colour, texture, shape feature extraction modules.

GLCM study and analysis.

Android compatibility for Image Processing.

Advantages and disadvantages of the current system and its scope.

Creating the Blue-Print of the system.

Setting the Project scope and proposal of the system.

Convincing Document.

Review paper.

Action planned in Week 1

Action Plan (for 2nd Sem) Based on the literature survey on various CBIR papers, various features like

Texture, Color and Shape are used for Image Analysis.

These features will be used for generating the feature vectors.

Implementing the generation of feature vectors.

Study on various CBIR engines currently in the market.

Enhancing the extraction capability of the existing system.

Compatibility on the Android platform.

Research on the machine learning implementation algorithms.

Automation annotation of the features.

Action planned in Week 1

CBIR Implementation The accuracy of a CBIR system depends on various factors like extraction of

appropriate features from the image, classification of the image.

The features extracted from an image are represented in the form of vectors.

The retrieval accuracy, computational complexity, retrieval time depend on the dimension of the feature vector.

Higher the dimension of feature vectors, better the retrieval accuracy but the memory for storage, retrieval time and computational complexity increases.

Thus is it important to find balance between the dimension of feature vectors and the accuracy with satisfactory storage and computational requirements.

Action planned in Week 2

Architecture of CBIR system

Image Databas

e

Feature Databas

e

Feature Extraction

Query Image

Feature Extraction of query image

Similarity Measures

Result

Action planned in Week 2

Algorithmic approach There are various approaches which have been experimented to generate

the efficient algorithm for image feature extraction in CBIR.

These approaches advocate different ways of extracting features of the images to improve the result in the form of better match of the query image in the large database.

Methods of feature extraction using Vector Quantization, bit truncation coding, Walsh Transform has also provided the new horizon to the feature extraction methodology.

The method of sectorization has been experimented on DCT, DST, DCT-DST Plane, Haar Wavelet, Walsh Wavelet and Kekre’s Transform.

These approaches can be compared and the retrieval performance can be evaluated.

Action planned in Week 3

Sectorization for DFT Steps of the algorithm are given below.Step1: Extract Red, Green and Blue components of the color image.

Step2: Apply the Transform DFT on individual color planes of image to extract feature vector.

Step3: All the components (matrix values) of each color planes are consider for the

sectorization process.

Step4: DFT generates complex value. Three matrices are generated based on the matrix

components i.e

o Complex matrix (real+imaginary values)

o Magnitude (absolute values)

o Phase (angle values)

Action planned in Week 4

Sectorization for DFT Step 4: Sectorize the DFT matrix based on the phase value (45,90,135,180)

i.e 4 sectors.

Step 5: Generate the matrix for sectorized value.

Step 6: Generate feature components (for each phase sector) as given below:o Component 1 : Complex vectors (real + imaginary)

o Component 2 : Magnitude vectors

o Component 3 : Phase vectors

Step 7: Generate the positive components and neglect the negative values.

A total of 12 components are created (4 phase sectors * 3 component vectors * 3 planes).

Step 8: Compare retrieval results for FV’s combined and independently (for all planes) .

Action planned in Week 5,6

Sectorization Representation

Split Image in R,G,B

component

Input RGB image (M * N * 3)

Apply DFT

DFT Matrix (M * N)

DFT Matrix (M * N)

Compute

• Complex matrix

• Magnitude matrix

• Phase matrix

Sectorization

Feature Compone

nt Database

Compare and

evaluate performanc

e parameters

Action planned in Week 5,6

Applications The tremendous use of images in the digital world of today has proved the

CBIR as very useful in several applications like Finger print recognition, Iris Recognition, face recognition, palm print recognition, speaker identification, pattern matching and recognition etc.

The CBIR technology has been used in several applications such as biodiversity information systems, digital libraries, crime prevention, medicine, his-torical research, among others.

References Dr. H.B.Kekre Dhirendra Mishra, “Image Retrieval using DST and DST

Wavelet Sectorization” published in International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, 2011 .

Dr. H.B.Kekre, Dhirendra Mishra “DCT-DST Plane sectorization of Row wise Transformed color Images in CBIR” published in International Journal on Computer Science and Engineering (IJCSE).

Vibha Bhandari1, Sandeep B.Patil, “CBIR Using DCT for Feature Vector Generation” published in International Journal of Application or Innovation in Engineering & Management (IJAIEM) Volume 1, Issue 2, October 2012.

Thank You !!

Queries if any ??