Why Use Categories?
People love categories!

Why Use Categories?
What if we didn’t have categories?
Humuhumunukunukuapua'a –
“fish that grunts like a pig”

Why Use Categories?
Our minds work very intimately with categories
– Every common noun in English is a category
– Proper nouns name object instances
– “this,” “that,” “the,” “my,” “yours,” etc. refer to object instances anonymously

The Categorization Problem

The Categorization Problem
Categorization/Classification:
Given a set of pre-defined categories, “bin” this image
Does not necessarily require object detection
Vertical Dimension:
1.
General: “Animal”
2.
Basic: “Bird”
3.
Specific: “Robin”

The Categorization Problem
What kinds of categorization are computers good at?
• Basic -- especially when using context clues
• Specific -- due to low intra-class variation

The Categorization Problem
Bad at?
• General, due to high intra-class variation and a lack of visual cues

The Categorization Problem
Bad at?
• Categories defined by non-visual characteristics
(like chairs)

Summary
• Semantic categories allow humans to convey a large amount of information concisely
• We want computers to be able to do the same
• What work has been done on this problem?
Has it been successful?

1. Using Context in Categorization
2. Fine-Grain Object Classification

Caltech 101 (2003)
• Dataset for basic-level categorization
• Objects from 101 classes
• Famously difficult

Categorization with Context
Goal: Resolve ambiguity between similarlooking objects of different classes using the
semantic context of an object
Rabinovich et al. (UC San Diego):
Objects in Context
First paper to attempt to use context at the object level
PASCAL 2007 dataset

Categorization with Context

Categorization with Context
Approach
1. Segment image to preserve some spatial data
2. Perform Bag-of-Features to give an initial ranked list of labels for each segment
3. Use a Conditional Random Field (CRF) framework to find agreement between segment labels

Categorization with Context

Bag-of-Features with Segmentation
Labeling Segments:
Confidence:

Conditional Random Field
Way to assign joint probabilities to elements without considering every possible combination in the training set

Conditional Random Field
Idea
• Given set of segments S, set of labels C
• Want to find p(C | S) without knowing p(S)
• Associate a special graph with C that obeys the “Markov Property” (uses S)
• The ordered pair (S, C) is a CRF conditioned on
S

Conditional Random Field

Results

Results
False correction

Fine-Grain Classification

Fine-Grain Image Categorization
Challenge: need good classifiers that capture detail well

Fine-Grain Image Categorization
Yao et al. (Stanford): Combining Randomization and
Discrimination for Fine-Grained Image Categorization
Approach
Random forest with discriminative classifiers
This is a kind of machine learning framework that allows us to handle the fine detail in this problem.

Fine-Grain Image Categorization

Random Discriminative Tree
Approach
• For each tree node, train an SVM classifier for a randomly sampled image region
• At each node, make a yes-or-no decision
• Uses grayscale SIFT descriptors

Random Discriminative Tree

Results

Conclusion
• Semantic categories allow humans to convey a large amount of information concisely
• Categorization has been used for basic-level object detection and scene recognition
• Fine-grain categorization can provide us with expert-level classification of objects
• Not all categories are defined by visual characteristics!

Questions?