Image Indexing and Retrieval

Multimedia Database Management System - Chapter 6

Image Indexing and RetrievalRachmat Wahid Saleh Insani, S.Kom


Objectives• Image indexing and retrieval approaches.

• Image retrieval based on text description.

• Image Indexing and Retrieval based on features representation (color, shape, and texture).

• Image similarity calculation.

• Image indexing and retrieval techniques based on compressed image data.

• Other image indexing and retrieval technique.

• Integrated image retrieval technique.


Image Indexing And Retrieval Approaches


Image Indexing and RetrievalApproaches

First approach: a set of attributes.



Second approach: An integrated feature-extraction/object-recognition subsystem.



Third approach: image annotation.



Fourth approach: low level image features.


Image Retrieval Based On Text Description


Text Based Image Retrieval


Color Based Indexing and Retrieval Technique

• The idea is to retrieve from database image that has perceptually similar colour to the user's query image or description.

• During retrieval, the distance between the histogram of the query image and image in the database are measured.

• A color histogram H(M) is a vector (h1, …, hj, … hn).

• hj, number of pixel of image M falling into bin j.

• hn, number of pixel of image M falling into all bin.

• Bin, is a discrete color combinations.


Color Based Indexing and Retrieval Technique

The simplest distance between images I and H is the L-1 metric, defined as

Example: We have three images of 8x8 pixels and each pixel is in one of eight colors C1 to C8. Image 1 has 8 pixels in each of the eight colors, Image 2 has 7 pixels in each of colors C1 to C4, and 9 pixels in each of colors C5 to C8. Image 3 has 2 pixels in each of colors C1 and C2, and 10 pixels in each of colors C3 to C8. Which two images are most similar and which two images are most different?

d(I ,H ) = | il − hl |l=1

n

∑


Improvements to the Basic Technique of Color Based IR

• Making use of similarity among colors.

• Making use of spatial relationships among pixels.

• Making use of the statistics of color distribution.

• Better color representation.


Image Retrieval Based on Shape

• Images are segmented into individual objects.

• The basic issue is shape representation and similarity measurement between shape representations.

• A good shape representation and similarity measurement for recognition and retrieval purposes have important properties:

- Each shape should have a unique representation, invariant to translation, rotation, and scale;

- Similar shapes should have similar representations so that retrieval can be based on distances among shape representations.

• The similarity measure between shape representations should conform to human perception.


Image Retrieval Based on Texture

• Texture is described by six features:

- coarseness, opposite to fine;

- contrast, dynamic range of gray level, ratio of bow areas, sharpness of edges, and period of repeating pattern;

- directionality, element shape and placement;

- line likeness, shape of a texture element;

- regularity, variation of an element placement rule;

- roughness.the texture is rough or smooth.


Image Indexing and Retrieval Based on Compressed Image Data

• There are three common compression technique for image indexing and retrieval:

- DCT Coefficient

- Wavelet Coefficient

- VQ Compressed Data


Other Techniques

• Image Retrieval Based on Model-Based Compression

• Image Retrieval Based on Spatial Relationship


Image Retrieval based on Model-based Compression

• An object, is represented by a mathematical model (parameter or mathematical equation).

• A very little data required for representing these parameters and equations, so a very high compression can be achieved.

• Image distance calculated by parameters differences.


Image Retrieval Based on Spatial Relationship

• A spatial relationship, specifies how some object is located in space in relation to some reference object.

• Example queries, “find images containing a sun above to a mountain”.

• Example application, Geographical Information System (GIS).


Integrated Image Indexing and Retrieval Techniques

Structured attributes Pictorial queries

are not supportedIntegrated IR Techniques +

relevance feedback

Text-annotation

Color based High-level abstractions in images are not

supportedShape based

Texture based

Technology

Image Indexing and Retrieval