Content-based Image Retrieval

Mei Wu

Faculty of Computer Science

Dalhousie University

Motivation The huge amount of images, resulting from the fast

development of multimedia and the wide spread of internet, makes user-labelled annotation method “mission impossible”.

People are seeking for automatic image retrieval methods which are based on images own contents, such as color, texture and shape, rather than manually-labelled annotations.

CBIR can be broadly used in areas, such as crime prevention, medical diagnosis, satellite imaging and online searching.

CBIR System Architecture

Image Content Representation

8 Base GET types GET grouping

Sample PGET (upper), JGET (lower)Image content representation

Two Samples

Query image The top ten retrieved images

Experimental ResultsPrecision(10) Recall(20)

GET Only GET, PSS GET, AW-PSS GET Only GET, PSS GET, AW-PSS

Building(04_25_1) 0.8 0.8 0.8 0.29 0.24 0.24

Flower(12_33_1) 0.9 0.8 1 0.48 0.48 0.52

Tree(15_19_1) 0.9 0.8 0.8 0.7 0.65 0.65

Mountain(15_47_1) 0.8 0.7 0.9 0.46 0.39 0.46

Airplane(20_20_1) 0.6 0.8 0.8 0.38 0.42 0.5

Ferry(2026_29_1) 0.9 0.9 0.9 0.92 0.92 0.92

Car(29_06_1) 0.5 0.7 0.9 0.5 0.75 0.81

Average 0.77 0.79 0.87 0.53 0.55 0.59

Precision(10) Recall(20)

ColorHist ColorCorr P-Shape ColorHist ColorCorr P-Shape

Building(04_25_1) 0.7 0.6 0.8 0.18 0.21 0.24

Flower(12_33_1) 1 0.7 1 0.12 0.56 0.52

Tree(15_19_1) 0.1 0.1 0.8 0.15 0.1 0.65

Mountain(15_47_1) 0.6 0.7 0.9 0.29 0.54 0.46

Airplane(20_20_1) 0.5 0.4 0.8 0.29 0.25 0.5

Ferry(2026_29_1) 0.5 0.9 0.9 0.54 0.92 0.92

Car(29_06_1) 0.5 1 0.9 0.63 0.81 0.81

Average 0.56 0.63 0.87 0.31 0.48 0.59

Shape features comparison

Shape/color comparison