Tamara Berg Retrieval 790-133 Language and Vision

Tamara BergRetrieval

790-133Language and Vision

How big is the web?

• The first Google index in 1998 already had 26 million pages

• By 2000 the Google index reached the one billion mark.

• July 25, 2008 – Google announced that search had discovered one trillion unique URLs

Slide from Takis Metaxas

How hard is it to go from one page to another?

• Over 75% of the time there is no directed pathfrom one random web page to another.

Kleinberg: The small-world phenomenon


• Over 75% of the time there is no directed pathfrom one random web page to another.• When a directed path exists its average length

is 16 clicks.• When an undirected path exists its average

length is 7 clicks.

Kleinberg: The small-world phenomenon


• Over 75% of the time there is no directed pathfrom one random web page to another.• When a directed path exists its average length

is 16 clicks.• When an undirected path exists its average

length is 7 clicks.• Short average path between pairs of nodes is

characteristic of a small-world network (“six degrees of separation” Stanley Milgram).

Kleiberg: The small-world phenomenon

Information Retrieval

• Information retrieval (IR) is the science of searching for documents, for information within documents, and for metadata about documents, as well as that of searching relational databases and the World Wide Web

Wikipedia

Slide from Takis Metaxas

The Anatomy of a Large-Scale Hypertextual Web Search Engine

Sergey Brin and Lawrence Page

“The ultimate search engine would understand exactly what you mean and give back exactly what you want.” - Larry Page

Google – misspelling of googol

The Google Search EngineFounded 1998 (1996) by two Stanford students

Originally academic / research project that later became a commercial tool

Distinguishing features (then!?):

- Special (and better) ranking

- Speed

- Size

Slide from Jeff Dean

The web in 1997

Internet was growing very quickly• “Junk results often wash out any results that a

user is interested in. In fact, as of November 1997, only one of the top four commercial search engines finds itself (returns its own search page in response to its name in the top ten results).”

The web in 1997

Internet was growing very quickly• “Junk results often wash out any results that a

user is interested in. In fact, as of November 1997, only one of the top four commercial search engines finds itself (returns its own search page in response to its name in the top ten results).” Need high precision in

the top results

Google’s first search engine

Components of Web Search Service

Components

• Web crawler

• Indexing system

• Search system

• Advertising system

Considerations

• Economics

• Scalability

• Legal issues

Slide from William Y. Arms

Web Searching: Architecture

Build indexSearch

Index to all Web pages

• Documents stored on many Web servers are indexed in a single central index.

• The central index is implemented as a single system on a very large number of computers

Examples: Google, Yahoo!

Web servers with Web pages

Crawl

Web pages retrieved by crawler


What is a Web Crawler?

Web Crawler

• A program for downloading web pages.

• Given an initial set of seed URLs, it recursively downloads every page that is linked from pages in the set.

• A focused web crawler downloads only those pages whose content satisfies some criterion.

Also known as a web spider


Simple Web Crawler Algorithm

Basic Algorithm

Let S be set of URLs to pages waiting to be indexed. Initially S is is a set of known seeds.

Take an element u of S and retrieve the page, p, that it references.

Parse the page p and extract the set of URLs L it has links to.

Update S = S + L - u

Repeat as many times as necessary.

[Large production crawlers may run continuously]


Indexing the Web Goals: Precision

Short queries applied to very large numbers of items leads to large numbers of hits.

• Goal is that the first 10-100 hits presented should satisfy the user's information need

-- requires ranking hits in order that fits user's requirements

• Recall is not an important criterion

Completeness of index is not an important factor.

• Comprehensive crawling is unnecessary


Concept of Relevance and Importance

Document measures

Relevance, as conventionally defined, is binary (relevant or not relevant). It is usually estimated by the similarity between the terms in the query and each document.

Importance measures documents by their likelihood of being useful to a variety of users. It is usually estimated by some measure of popularity.

Web search engines rank documents by a weighted combination of estimates of relevance and importance.


Relevance

• Words in document (stored in inverted index)• Location information – for use of proximity in

multi-word search.• In page title, page url?• Visual presentation details – font size of

words, words in bold.

Relevance

<a href="http://www.cis.cornell.edu/">The Faculty of Computing and Information Science</a>

The source of Document A contains the marked-up text:

The anchor text:

The Faculty of Computing and Information Science can be considered descriptive metadata about the document:

http://www.cis.cornell.edu/


Anchor Text

Importance - PageRank Algorithm

Used to estimate popularity of documents

Concept:

The rank of a web page is higher if many pages link to it.

Links from highly ranked pages are given greater weight than links from less highly ranked pages.


Intuitive Model (Basic Concept)

Basic (no damping)

A user:

1. Starts at a random page on the web

2. Selects a random hyperlink from the current page and jumps to the corresponding page

3. Repeats Step 2 a very large number of times

Pages are ranked according to the relative frequency with which they are visited.


PageRank

Example

1 2

4

3

5

6

Basic Algorithm: Matrix Representation


Basic Algorithm: Normalize by Number of Links from Page


Basic Algorithm: Normalize by Number of Links from Page


Basic Algorithm: Weighting of Pages

Initially all pages have weight 1/n

w0 = 0.17

0.17

0.17

0.17

0.17

0.17

Recalculate weights

w1 = Bw0 = 0.06

0.21

0.29

0.35

0.04

0.06

If the user starts at a random page, the jth element of w1 is the probability of reaching page j after one step.




w0 = 0.17

0.17

0.17

0.17

0.17

0.17

Recalculate weights

w1 = Bw0 = 0.06

0.21

0.29

0.35

0.04

0.06





w0 = 0.17

0.17

0.17

0.17

0.17

0.17

Recalculate weights

w1 = Bw0 = 0.06

0.21

0.29

0.35

0.04

0.06



Basic Algorithm: Iterate

Iterate: wk = Bwk-1

0.01

0.32

0.46

0.19

0.01

0.01

0.01

0.47

0.34

0.17

0.00

0.00

->

->

->

->

->

->

0.00

0.40

0.40

0.20

0.00

0.00

w0 w1 w2 w3 ... converges to ... w

At each iteration, the sum of the weights is 1.

0.17

0.17

0.17

0.17

0.17

0.17

0.06

0.21

0.29

0.35

0.04

0.06


Special Cases of Hyperlinks on the Web

There is no link out of {2, 3, 4}

1

2

34

5 6


Google PageRank with Damping

A user:

1. Starts at a random page on the web

2a. With probability 1-d, selects any random page and jumps to it

2b. With probability d, selects a random hyperlink from the current page and jumps to the corresponding page

3. Repeats Step 2a and 2b a very large number of times

Pages are ranked according to the relative frequency with which they are visited.

[For dangling nodes, always follow 2a.]


Teleport!

The PageRank Iteration

The basic method iterates using the normalized link matrix, B.

wk = Bwk-1

This w is an eigenvector of B

PageRank iterates using a damping factor. The method iterates:

wk = (1 - d)w0 + dBwk-1

w0 is a vector with every element equal to 1/n.


Choice of d

Conceptually, values of d that are close to 1 are desirable as they emphasize the link structure of the Web graph, but...

• The rate of convergence of the iteration decreases as d approaches 1.

• The sensitivity of PageRank to small variations in data increases as d approaches 1.

It is reported that Google uses a value of d = 0.85 and that the computation converges in about 50 iterations


Image retrieval

Types of queries

1) Text query based retrieval

2) Image query based retrieval

1) Text query retrieval

2) Image query retrieval

Content based image retrieval:• Analyze visual content of images

– Extract features – Build visual descriptor of each image (query and database

images).

• For a query image, match descriptors between query and database images.

• Return closest matches in ranked order by similarity.

Image query retrieval

Query Image

Reminder: Image Representation

Represent the image as a spatial grid of average pixel colorsConvert data base of images to this representationRepresent query image in this representation.Find images from data base that are similar to query.

Photo by: marielito

http://flickr.com/photos/eni/


Query Image

Database Images


Query Image

Ranked Results – database images ranked by similarity to query


• What’s easy?

• What’s difficult?

Image Retrieval

• Image relevance

• Image importance

Image Retrieval

• Image relevance


Text info

• Idea – most images have associated text.• Analyze words around picture & associated with picture (title,

words, links, etc). • For a query word return pictures based on standard web

search on text associated with image.

Human info

Just leave the content analysis/labeling to people.

ESP gameLuis von Ahn, Ruoran Liu and Manuel Blum

http://www.gwap.com/gwap/gamesPreview/espgame/

User data

Watch what people click on!

Tags: banana, monkey banana, monkeys, primate, homeless, cardboard, funny photo, orangutan, hairy, brown, zoo, animal, brown fur …

Flickr – over 2 billion user uploaded pictures, 2+ million uploads per day. About half come with some sort of associated text.

Photo by haskins

Web – billions of web pages almost always containing text and images.

Text+Image info

Image Retrieval

• Image relevance


PageRank

• For web pages – use links between two pages as a measure of their similarity.

• For images – use number of matching features between two images as a measure of their similarity.– Features – SIFT features (based on histograms of

edges in different directions).– Two features are considered matching if their SSD

distance is below a threshold.

Pros/Cons

• Where will it work well?

• Where will it fail?

• What happens to polysemous queries?

• What about logos?

Text + Image PageRank

How could we extend this algorithm to incorporate image and text information?

Animals on the Web

Tamara L. Berg & David Forsyth

I want to find lots of good pictures of monkeys…

What can I do?

Google Image Search -- monkey

Circa 2006




Words alone won’t work

Flickr Search - monkey

• Even with humans doing the labeling, the data is extremely noisy -- context, polysemy, photo sets

• Words alone still won’t work!

Our Results

General Approach

- Vision alone won’t solve the problem. - Text alone won’t solve the problem.

-> Combine the two!

Animals on the Web

Extremely challenging visual categories.

Free text on web pages.

Take advantage of language advances.

Combine multiple visual and textual cues.

Goal:

Classify images depicting semantic categories of animals in a wide range of aspects, configurations and appearances. Images typically portray multiple species that differ in appearance.

Animals on the Web Outline:

Harvest pictures of animals from the web using Google Text Search.

Select visual exemplars using text based information.

Use visual and textual cues to extend to similar images.

Harvested Pictures

14,051 images for 10 animal categories.

12,886 additional images for monkey category using related monkey queries (primate, species, old world, science…)

Text Model

Latent Dirichlet Allocation (LDA) on the words in collected web pages to discover 10 latent topics for each category.

Each topic defines a distribution over words. Select the 50 most likely words for each topic.

1.) frog frogs water tree toad leopard green southern music king irish eggs folk princess river ball range eyes game species legs golden bullfrog session head spring book deep spotted de am free mouse information round poison yellow upon collection nature paper pond re lived center talk buy arrow common prince

Example Frog Topics:

2.) frog information january links common red transparent music king water hop tree pictures pond green people available book call press toad funny pottery toads section eggs bullet photo nature march movies commercial november re clear eyed survey link news boston list frogs bull sites butterfly court legs type dot blue

Select ExemplarsRank images according to whether they have these likely

words near the image in the associated page (word score)

Select up to 30 images per topic as exemplars.

2.) frog information january links common red transparent music king water hop tree pictures pond green people available book call press ...

1.) frog frogs water tree toad leopard green southern music king irish eggs folk princess river ball range eyes game species legs golden bullfrog session head ...

SensesThere are multiple senses of a category within

the Google search results.

Ask the user to identify which of the 10 topics are relevant to their search. Merge.

Optional second step of supervision – ask user to mark erroneously labeled exemplars.

Image Model

Match Pictures of a category

Geometric Blur Shape Feature

Sparse Signal Geometric Blur

(A.) Berg & Malik ‘01

Captures local shape, but allows for some deformation. Robust to differences in intra category object shape.

Used in current best object recognition systems Zhang et al, CVPR 2006Frome et al, NIPS 2006

Image Model (cont.)Color Features: Histogram of what colors appear in the image

Texture Features: Histograms of 16 filters

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Scoring ImagesRelevant Features

Irrelevant Features

Relevant Exemplar

For each query feature apply a 1-nearest neighbor classifier. Sum votes for relevant class. Normalize.

Combine 4 cue scores (word, shape, color, texture) using a linear combination.

Query

*

Irrelevant Exemplar

Classification ComparisonW

ords

Wor

ds +

Pic

ture

Cue Combination:Monkey

Cue Combination:

FrogGiraffe

Re-ranking Precision

Classification Performance

Google

Re-ranking Precision

Monkey Category

Classification Performance

Google

Monkey

Ranked Results:

http://tamaraberg.com/google/animals/index.html

http://tamaraberg.com/google/animals/index.html

Commercial systems

• http://tineye.com/login• http://labs.systemone.at/retrievr/• http://www.polarrose.com/• http://images.google.com• http://www.picsearch.com/

http://tineye.com/login

http://tineye.com/login

http://labs.systemone.at/retrievr/

http://www.polarrose.com/

http://images.google.com/

http://www.picsearch.com/