Scene Identification based onConcept LearningBeatrice van Eden

- Part time PhD Student at the University of the Witwatersrand.- Fulltime employee of the Council for Scientific and Industrial Research.

Index• Broad problem statement Give an overview of the researchI am busy with. • Identify items in a scene Current work on the project.

• Continued workHighlights of some of the reading I did.

Ivan Laptev2007

Broad Problem Statement

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

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

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

Identify items in a scene

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

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)

Thank you