Game with A Purpose

Two games are designed to collect semantic tags.

First game is analogous to Peek-A-Boom game:

•Inverse-Problem game

•Boom:

–Give a image with city name (from panoramio probably)

–Ask to point some predefined attributes (tree, building,…etc.) for peek

•Peek

–Answer the location of the city with as least of number of attributes given as possible

•Effect:

–Collect location of most representative objects for the place

The second game is analogous to Herd-It game:

•Given one query image (with or w/o ground truth location) and multiple players

•Game:

–Ask which of k other images matches query image the best.

•Effect:

–Collect relational similar image of query image and output the possible location of query image at the same time

Social Mobilization + GWAP - VFYW breaker:

•Crowd sourcing the answer of VFYW game by playing the game (such as the second game)

•Collect useful tags at the same time

•The highest score player has the right for own the answer to VFYW contest

Geometric Feature Pruning

Geometric Feature Pruning uses the semantic tags on maps to form a feature based on geometric relationship of tags to reduce search space of image localization problem.

Semantic tags:
Google map API allows us to extract semantic tag such as
road
man-made building...etc
Google Style Map Spec

Geometric Feature:
In Madrid example, the angle between road is used.

Experimental Setup:
The Madrid example is used to verify the idea.

The full search space is a rectangle around the ground truth location.

The search space is 425 m in width and height which is about 0.03 of size (m^2/m^2) of city.

Semantic map:

(a) road

(b) building and space

We have 9 maps with the same size of example image to cover the search space.

Geometric feature:

We develop an intersection descriptor which can be automatically extracted from styled Google Map.

Two features in intersection descriptor can be used for pruning search space.

1. the number of corners at the intersection.

2. the angle of the corner.

Experiment:

Query Intersection is computed from rectified images.

Here "Number of Corner: 2" and "Angle: 73.88" are used to prune the search space.

The ground truth location is at the center of image.

The degree of matching score can be represented by radius of blue circle.

The radius r is computed by

r = R * exp(-d(ang0,ang1)/sigma)

where R, sigma is a constant. d(. , .) is 2 norm distance of query angle and angle in database.

Another Example: Paris

The Paris Example

Query: "Number of Corner: 4" and "Angle: 57.68" are used to prune the search space.

Result:

The ground truth location is at the center of image.

Bibliography

Image-based localization:
From Structure-from-Motion Point Clouds to Fast Location Recognition CVPR'09
Location Recognition using Prioritized Feature Matching ECCV'10
Fast Image-Based Localization using Direct 2D-to-3D Matching ICCV'11

3D reconstruction:
Piecewise Planar and Non-Planar Stereo for Urban Scene Reconstruction
- multiview stereo with peice-wise constraint
- not talking about roof

Fusion of feature- and area-based information for urban buildings modeling from aerial imagery
- have all data (range data)
manhattan world stereo
- Piece-wise planer assumption everywhere

Blocks World Revisited: Image Understanding Using Qualitative Geometry and Mechanics
- claim only qualitative reconstruction is feasible and "impossible for metric reconstruction from a single image"
- aim at a fully automatic system

Closing the Loop in Scene Interpretation
-

VFYW Dataset

Introduction:

VFYW Dataset is designed for Image localization problem. The dataset currently consists of 73 query images labeling with ground truth location and negative locations. The ground truth location is represented by city and geographic coordinate. Negative locations were collected from wrong guesses and rephrased into city to avoid ambiguity. Other negative locations can be obtained by the results feeding query image into Google Search by Image.

All query images and human responses are collected from VFYW contest and will update weekly with the contest.

The degree of difficulty of query images can be assigned with three levels by the precision of winner's answer:
a. No one Correct
No one answered the correct city. (9/71)
b. Up to a City
The winner answered the correct city but not provide the exact location. (12/71)
c. Exact location
The winner gave exact location of query image. (50/71)

Here are some sample images of different degree of difficulty.
a. No one Correct

b. Up to a City

c. Exact location

Here an example (#71) is shown to illustrate VFYW dataset.

Query image (solved with exact location)

Ground truth location:

'Dhaka, Bangladesh'

'23.754239,90.392075'

Negative location (guesses from human)

'Tripoli, Libya'

'Maputo, Maputo City, Mozambique'

'Kinshasa, Democratic Republic of Congo'

'Dar es Salaam, Tanzania'

'Iquitos, Peru'

'Managua, Nicaragua'

'Cairo, Egypt'

'Beirut, Lebanon'

'Famagusta , Cyprus'

'Bangkok, Thailand'

'Mumbai, India'

Negative location (Google search by image, keyword 'google map panoramio' is added to ensure every retrieved image with geo tag)

VFYW #70 (Baby Step)

VFYW # 70: Edinburgh, UK

The query image:

The GUI helps human perform metrology to query image.

The precise scale of ROI region of overhead view can be reconstructed by image metrology.

Match reconstructed overhead image to satellite image provided by Google Map.

The reconstructed overhead view in this image shows the road in query image is curve. This clue can filter can reduce search space given many road goes straight.

The above image is road map in Edinburgh. Search space can be reduced by only looking at road with certain curvature.

VFYW #57 (Baby Step)

VFYW # 57: Muskegon, MI

The query image:

The GUI helps human perform metrology to query image.

The precise scale of ROI region of overhead view can be reconstructed by image metrology.

Match reconstructed overhead image to satellite image provided by Google Map.

In this case, the reconstructed overhead  image assists human to find out the exact matching between ground image and satellite image. Furthermore, the camera position can also be estimated by looking at the homography. If the labels of 'Ocean' and 'Ground' are given in both query and satellite image, some automatically indexing algorithm can be developed.

Discussion ["Will This Be The End Of The VFYW Contest?" Ctd]

"Will This Be The End Of The VFYW Contest?" Ctd

Hey, I am here try to win all of your books. : p