Objects as Attributes for Scene Classification

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Presentation transcript:

Objects as Attributes for Scene Classification Yongsub Lim Applied Algorithm Lab., KAIST 2019-01-18

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 2019-01-18

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 2019-01-18

Introduction Not easy to distinguish these scenes based on just texture statistics! 2019-01-18

Object bank representation Object filters are to characterize local image properties related to the presence/absence of objects 2019-01-18

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 2019-01-18

Object bank representation OB achieve reasonable recognition results on a very small number of scene training examples 2019-01-18

Object bank representation OB is also good for a small number of object detectors 2019-01-18

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 2019-01-18

Objects are not equally important In this paper, 200 most frequent objects obtained from popular image datasets and image search engines are used 2019-01-18

Hierarchy of Selected Objects 2019-01-18

Experiments: basic-level This paper uses a much simpler classifier than SPM 2019-01-18

Experiments: basic-level OB is not a replacement of low-level image features, it offers important complementary information of the images 2019-01-18

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 2019-01-18

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 2019-01-18

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 2019-01-18