1. Content-based Image Retrieval Mei Wu Faculty of Computer Science Dalhousie University

2. 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.

3. CBIR System Architecture

4. Image Content Representation 8 Base GET types GET grouping Sample PGET (upper), JGET (lower)Image content representation

6. Two Samples Query imageThe top ten retrieved images

7. Experimental Results Precision(10)Recall(20) GET OnlyGET, PSSGET, AW-PSSGET OnlyGET, PSSGET, AW-PSS Building(04_25_1)0.8 0.290.24 Flower(12_33_1)0.90.810.48 0.52 Tree(15_19_1)0.90.8 0.70.65 Mountain(15_47_1)0.80.70.90.460.390.46 Airplane(20_20_1)0.60.8 0.380.420.5 Ferry(2026_29_1)0.9 0.92 Car(29_06_1)0.50.70.90.50.750.81 Average0.770.790.870.530.550.59 Precision(10)Recall(20) ColorHistColorCorrP-ShapeColorHistColorCorrP-Shape Building(04_25_1)0.70.60.80.180.210.24 Flower(12_33_1)10.710.120.560.52 Tree(15_19_1)0.1 0.80.150.10.65 Mountain(15_47_1)0.60.70.90.290.540.46 Airplane(20_20_1)0.50.40.80.290.250.5 Ferry(2026_29_1)0.50.9 0.540.92 Car(29_06_1)0.510.90.630.81 Average0.560.630.870.310.480.59 Shape features comparison Shape/color comparison