1. Objects as Attributes for Scene Classification
Yongsub Lim
Applied Algorithm Lab., KAIST

2. Introduction
Low-level features have used for visual tasks, but not enough as those become higher level
This paper proposes to use objects as attributes of scenes for scene classification
Objects become attributes of scenes

3. Introduction
Attribute based methods for object recognition have shown promising results
For instance, polar bear can be described as white, fluffy object with paws
Such visual attributes summarize the low-level features into object parts, and other properties

4. Introduction Not easy to distinguish these scenes
based on just texture statistics!

5. Object bank representation
Object filters are to characterize local image properties related to the presence/absence of objects

6. Comparing to other popular methods
Low-level based methods produce very
similar results for images having very
different meaning
OB easily distinguish due to the
semantic information

7. Object bank representation
OB achieve reasonable recognition results on a very small number of scene training examples

8. Object bank representation
OB is also good for a small number of object detectors

9. What are ‘objects’ for object filters?
More objects, better performance
Semantic hierarchy becomes more prominent
It is not a good way
One observation is that not all objects are of equal importance in natural images
Just need detectors for a few most important objects
There is a result that 3000~4000 concepts are enough to annotate video data

10. Objects are not equally important
In this paper, 200 most frequent objects obtained from popular image datasets and image search engines are used

11. Hierarchy of Selected Objects

12. Experiments: basic-level
This paper uses a much simpler classifier than SPM

13. Experiments: basic-level
OB is not a replacement of low-level image features, it offers important complementary information of the images

14. Experiments: super-ordinate level
It is tested on UIUC-Sports dataset
Activities and events become classes
OB is even better than state-of-the-art 73.4% which uses all given object outlines and identities

15. Experiments: super-ordinate level
We can see that a more semantic-level image representation overcomes confusion caused by low-level features
eg. sailing and rowing

16. Summary
Consider objects as attributes of scenes, use object bank representation for images
Need a modest number of objects which occurs much more frequently than the majority
OB is not only good itself, but also because it provides information which low-level features did not capture, it can boost performance significantly by combining features