The document summarizes Beatrice van Eden's research on scene identification based on concept learning. It discusses her work using image recognition techniques to identify items in a scene and form concepts of the environment. It also highlights some of the papers she has been reading on continued work related to object detection in 2D and 3D images, semantic mapping using object segmentation, and extracting editable objects from a single photo.

1. Scene Identification based on
Concept Learning
Beatrice van Eden
- Part time PhD Student at the University of the Witwatersrand.
- Fulltime employee of the Council for Scientific and Industrial Research.

2. Index
• Broad problem statement
Give an overview of the research
I am busy with.
• Identify items in a scene
Current work on the project.
• Continued work
Highlights of some of the reading
I did.
Ivan Laptev
2007

3. Broad Problem Statement
• How can we give a mobile robot the capability to
continuously and autonomously form concepts
of its environment?

4. Identify items in a scene
Image of scene
Identify items in
scene
Determine most
feasible scene
Recognise the scene
/ contribute to a
concept
• Ivan Laptev
• Recognize and to localize objects in a still image (2D).
• Histogram based image representations.
• They used AdaBoost to select histogram regions optimized for the
classification of training samples.
• Fisher weak learner is then applied to select a histogram feature
and an associated classifier at each round of AdaBoost

5. Identify items in a scene
• Steve's Object Detection Toolbox 1 (Steve Branson)
• Features, Object Recognition, and Object Detection.
• The following features are included. They can be used in
conjunction with object recognition, sliding window detection, or
deformable part models (e.g., localized versions of features are
supported).
• HOG: Dalal Triggs-style HOG detector, with tricks to compute them
quickly over multiple orientations
• Bag of Words SIFT: Sliding window detectors over vector quantized
SIFT descriptors
• Color Histograms: Sliding window detectors over RGB or CIE color
histograms
• Fisher Vectors: Fisher vector encoded SIFT or color features
(Perronin et al. ECCV'2010)
• Spatial Pyramids: The above features can be stacked together in
spatial pyramids, or multi-resolution pyramids

6. Identify items in a scene

7. Continued work
Searching for objects
driven by context
(Bogdan Alexe and Heess, Nicolas and
Yee W. Teh and Vittorio Ferrari)
• Still 2D Images
• Video image detection and tracking
• 3D Images
• Recap face tracing algorithm
• Recap tabel top detection and cluster recognition on table top

8. Continued work
Searching for objects
driven by context
(Bogdan Alexe and Heess, Nicolas and
Yee W. Teh and Vittorio Ferrari)
Scemantic Mapping Using
Object-Class Segmentation
of RGB-D Images
(Stuckler, J.; Biresev, N.; Behnke, S)
Unsupervised feature learning for 3D scene labeling
(Lai, K.; Liefeng Bo; Fox, D)
3-Sweep: extracting editable
objects from a single photo
(Tao Chen, Zhe Zhu, Ariel Shamir, Shi-Min
Hu, Daniel Cohen)

9. Thank you