A SEARCH ENGINE FOR WEB IMAGES USING DOCUMENT
TEXT STEMMING
Ryan Hardt, Ethan V. Munson
Department of EECS, University of Wisconsin-Milwaukee, 2200 E. Kenwood Blvd., Milwaukee, WI, U.S.A.
Hien Nguyen
Department of Mathematical and Computer Sciences, University of Wisconsin-Whitewater, Milwaukee, WI, U.S.A.
Keywords: Stemming, image retrieval, text-based image retrieval, information retrieval.
Abstract: A Web image search application was built using a previously-developed image relevance model for
retrieval of images via text-based image retrieval. The application includes a text stemmer that converts a
word to a canonical form, making it possible to match text in the face of changes in tense or plurality that
have little effect on semantics. The usefulness of stemming in Web image retrieval was evaluated via a test
on ten queries that were submitted both with and without stemming. Relevance of retrieved images was
determined via ratings by three trained individuals. With stemming, the average unique relevance recall (a
measure of the proportion of relevant images returned by one algorithm and not another) was 27.7%, while
without stemming, it was only 0.5%. These results may more accurately apply to queries containing at least
one plural noun, present tense verb, present participle verb, or past tense verb.
1 INTRODUCTION
Google Image Search (Google, 2007), and Yahoo
Search - Image Search (Yahoo! Inc., 2007) are
image retrieval search engines (Zhang, 2005) that
are actually text-based. They take advantage of the
fact that images on Web pages are inherently
annotated by text within HTML documents. Text
describing images present on a Web page is often
contained within the document. It has been shown
that certain locations in an HTML document are
particularly likely to contain these annotations, such
as the image filename, page title, or text surrounding
the image (Thao, 2003). This approach to text-based
image retrieval does not require a formal image
annotation or content analysis process. Because of
this, existing Web content can be used without
requiring manual annotation of images that are
already present on the Web.
When Web pages are searched for text matching
a query, each word in the query is compared to each
word in the Web page in an attempt to find a match.
However, if the user enters the word "crying" in the
query, this will not match other forms of this word
like "cry," "cries," or "cried." Additionally, a
common convention in naming multiple files of
similar subject matter is to append an underscore
followed by a sequence number. An example of this
for an image filename may be "crying_02.jpg." A
straight forward word-for-word comparison would
not match this image filename to the input query of
"crying." These short-comings could potentially
cause desirable images to be left out of the result set.
Our research expands upon work that has shown
that the combination of the structure of HTML
documents and image locations within those
documents can be used to determine the relevance of
an image based on a given query (Thao, 2003).
Through analysis of HTML features using a
collection of 2,400 Web pages and 5,806 images
gathered by queries returned from a Google Web
page search (Google, 2007), a statistical model
combining the most useful features was developed.
This paper discusses the use of this statistical model
in building a search engine for images on the Web.
This search engine makes use of a technique called
"stemming," in which any word in the query or Web
page is reduced to its stem. A stem is a canonical
form that all other variations of a given word will be
223
Hardt R., V. Munson E. and Nguyen H. (2008).
A SEARCH ENGINE FOR WEB IMAGES USING DOCUMENT TEXT STEMMING.
In Proceedings of the Fourth International Conference on Web Information Systems and Technologies, pages 223-230
DOI: 10.5220/0001526502230230
Copyright
c
 SciTePress

reduced to if processed by the same stemming
algorithm. By stemming both the query and returned
Web pages, relevant images will not be omitted from
the result set simply because of variations in word
form between a Web page and the query.
Because Web pages contain non-semantic text,
stemming techniques used on traditional text
documents are not necessarily directly applicable.
While some research has shown that stemming is
successful when searching for images using queries
of certain lengths on some datasets (Hull, 1995) or
when substantial search techniques are applied in
addition to traditional text-based searching (Kraaij,
1996), other research claims that stemming has little
effect (Harman, 1991). We aim to test our
hypothesis that stemming is beneficial for text-based
image retrieval using queries containing specific
parts of speech.
As part of this research, we implemented a
set of software tools in order to:
 Allow the user to submit a one or two word
query
 Stem the query and the text in a Web page
 Obtain an image relevance rating
 Provide an interface to view results
2 BACKGROUND AND
RELATED WORK
An image retrieval system is one in which a query is
made to a system in an attempt to find images that
correspond to that query. Image content must be
translated in some way to allow for comparison to
the search query. It is in identifying both the relevant
image features as well as the information source that
makes this translation challenging.
In this section, we will discuss research in which
an image relevance model for an image retrieval
system was developed as well as the concept behind
stemming.
2.1 Previous Project Work
This paper continues research in which a relevance
model for image search on the Web was developed.
Query text was searched for among 53 different
HTML formatting features, such as image filename,
page title, and image ALT attribute. Using stepwise
logistic regression, a relevance model containing 13
independent variables was developed.
Table 2.1: Twenty-four queries and their categories used
to generate data.
ID Query Category
1 Bill Gates Famous People
2 George Bush Famous People
3 Britney Spears Famous People
4 John White Less Famous People
5 Michael Brown Less Famous People
6 William Black Less Famous People
7 New York Famous Places
8 Michigan Lake Famous Places
9 Yellowstone Park Famous Places
10 Spokane Less Famous Places
11 Burlington Vermont Less Famous Places
12 Haw River Less Famous Places
13 New Year Holiday
14 Thanks Giving Holiday
15 Halloween Holiday
16 Happy Child Concept
17 Sad Woman Concept
18 Burning House Concept
19 Raining Phenomenon
20 Volcano Erupt Phenomenon
21 Bomb Explode Phenomenon
22 Eiffel Tower Landmarks
23 Vietnam Memorial Landmarks
24 Statue Liberty Landmarks
2.1.1 Query Selection
To develop this model, a set of 24 queries belonging
to one of eight different categories was chosen as
shown in Table 2.1. The variety of query categories
was chosen in an effort to evaluate possible
differences in effectiveness among different types of
image content.
2.1.2 Image Downloading and Result
Storage
With over 11.5 billion Web pages present on the
Web (Gulli, 2005), the task of building a system to
collect a representative sample of the Web would be
very difficult. Because of this, the Google Web page
search engine was used as an information source. A
command line interface was used to extract up to
1000 URLs returned by Google and randomly select
the URLs of 100 pages to download.
Each of these links was visited and its contents
downloaded. Both the HTML document and all of
its images were stored locally. A database was used
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to store information about the downloaded web
pages, images, and the image ratings.
2.1.3 Document Analysis
After all HTML documents and images were
downloaded, they were examined to discover which
HTML features are useful for determining image
relevance. A total of 53 HTML features were
identified as possibly useful in a relevance model.
After the images were rated for relevance by human
raters, each of the 53 features was analyzed using
frequency-based statistics and logistic regression,
which will be discussed in the next section. Based
on this analysis, 13 of the original features were
determined to be useful in determining image
relevance. These features are shown in Table 2.2.
Text in the search query was compared to text in
each of these features in a given Web page returned
from Google. All features except for image
filename, image path, page filename, and page path
were allowed to return a match with the presence of
a maximum of 20 characters between any two query
terms. This allows adjectives or adverbs to appear
between query terms in the Web page without
preventing a match. These terms must, however,
appear in the same order as they appear in the query.
The 4 previously mentioned features must contain at
least one matching term to return a match. For every
image, each feature was assigned either a 1 if the
feature was present in relation to the image and
contained text matching the query text, or a 0
otherwise. These values were computed for all
features for all images and recorded in the database.
2.1.4 Data Analysis
Each of the 5,806 images was rated by three human
raters who were instructed on how to rate the
images. Images received one of three ratings:
relevant, somewhat relevant, or irrelevant. For the
purpose of data analysis, an image was considered
relevant if it was rated relevant by at least two of the
three human raters.
Each of the 53 HTML features was examined
independently using frequency-based statistics. The
frequency at which the HTML features both
occurred in the documents and contained matching
query text or did not occur in the documents was
recorded. This information was cross-tabulated with
the relevance ratings of the three human raters to
identify 19 HTML features that appeared to be
useful for the construction of a data model.
Table 2.2: Thirteen useful HTML features for determining
image relevance.
ID Feature
1 Image Filename
2 Page Title
3 Page Filename
4 ALT Attribute
5 Image Path
6 Page Path
7 Cell Below
8 Meta Description
9 Cell Above
10 Other Body Text
11 Anchor Text
12 Cell Right
13 Cell Left
Logistic regression identifies the linear combination
of a set of independent variables to determine the
probability of an event occurring. For this research,
the set of independent variables was the HTML
features and the event is the successful returning of
an image based on a given query. Through a process
called forward stepwise regression, each feature was
analyzed and added to an equation one at a time
according to its improvement to the model. The
resulting formula used 13 of the remaining 19
HTML features. This process resulted in a data
model for determining image relevance.
2.2 Stemming
Stemming is the process of reducing a word to its
stem. A typical English word contains a stem, which
refers to the central meaning of the word. In addition
to a stem, a word also consists of affixes, or prefixes
and suffixes. These affixes are appended to allow
slightly different meanings or usages of the stem. All
forms of a given word will ideally result in the same
term when stemmed using the same stemming
algorithm. This stem does not have to be a word
itself. A collection of related words and their stems
can be seen in Table 2.3.
Table 2.3: Various stems and the words that generated
them.
Stem Words
cri cry, cries, crying, cried
bike bike, bikes, biking, biked
run run, runs, running
dog dog, dogs
Words are typically composed of a prefix, root,
and suffix. Prefixes are considered to have more
A SEARCH ENGINE FOR WEB IMAGES USING DOCUMENT TEXT STEMMING
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influence over the meaning of a word. Thus, their
removal from a word can drastically alter the
meaning of a word. On the other hand, suffixes
generally do not provide a substantial amount of
meaning to a word. Therefore, the removal of
suffixes is the focus of most stemmers. English has
two main types of suffixes, inflectional and
derivational (Tars, 1976). Common
endings for both
types of suffixes can be seen in Table 2.4 and Table
2.5. While it should be noted that some specific
domains have suffixes that provide important
information about the word, suffix removal is
generally acceptable in stemming.
Table 2.4: Common inflectional suffixes.
Plural Nouns -s, -es, -ies
Present Tense Verbs -s, -es, -ies
Present Participle Verbs -ing
Past Tense Verbs -ed, -ied
Table 2.5: Common derivational suffixes.
Nominalizations -ing, -ion, -ment,
-ist, -ness, -ship
Adjectivals -al, -ic, -ful, -ous
Adverbials -ly
Verbals -ize
There are two types of errors subject to
stemming: understemming and overstemming (Paice,
1994). Understemming occurs when a stemming
algorithm does not reduce words that refer to the
same concept to the same stem. Stemmers that are
prone to this type of error are referred to as "light
stemmers." Overstemming occurs when a stemming
algorithm reduces words that refer to different
concepts to the same stem. Stemmers that are prone
to this type of error are referred to as "heavy
stemmers." For this application, we used a light
stemmer to reduce the probability of obtaining
undesirable matches.
3 METHODOLOGY
The goal of this project was to build a Web
application that allowed users to search for images
both on the Web and on a local image collection and
to explore the usefulness of stemming in the area of
text-based image retrieval. To accomplish this, it
was necessary to: implement a collection of tools to
search and parse Web documents, create an interface
to search a locally stored image collection, create a
Web interface, and implement a stemming
algorithm. This section will address these issues.
3.1 Accessing Content from the Web
When a query is submitted, a request is sent to
Google for a Web page search. The format of the
returned page was examined to discover the HTML
structure in which the returned links were specified.
This HTML document is then parsed and the
returned links extracted and stored in a list. Only the
first 100 URLs returned are stored due to the time
required to parse each page.
3.2 Accessing Locally Stored Web
Pages
In addition to online Web image retrieval, this
application also provides an interface to an existing
collection of 2,400 Web documents and 5,806
images generated and stored by Thao (Thao, 2003).
Our application allows queries to be sent to this
database. Only the original 24 queries can be
submitted for a search against the Web pages stored
in the database. Instead of obtaining a list of URLs
from Google, the user receives a list of local file
paths from the database that point to the downloaded
Web documents.
3.3 Stemming Software
To stem text, we used the Snowball stemming
algorithm (Porter, 2007), which is based on the
Porter algorithm (Van Rijsbergen, 1980). This
algorithm is regarded as the de-facto standard in
English stemming for information retrieval systems.
Even if stemming is desired, the query remains
unstemmed when sent to Google. Also, for some
words, the stemmed form returned by our software is
not a standard English word, which would make a
standard text search substantially less accurate. It is
important that we are examining the same dataset
when comparing image retrieval with and without
stemming. If the query were stemmed before being
sent to Google, the pages returned would be
different from a dataset generated by an unstemmed
query, and the pages would likely be of varying
content.
Stemming is implemented on each of the 13
evaluated HTML features individually. When a
feature is being explored for the presence of query
text, it first has all punctuation removed, since the
algorithm will not appropriately stem any word
containing punctuation. It is important that each
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HTML feature is extracted from the Web document
and stored appropriately in a Web page object before
the document is stemmed. If the document was
stemmed before the HTML feature extraction, the
HTML tags would no longer be present as expected,
and the features would be difficult to extract. If a
punctuation mark is found in one of the HTML
features, it is replaced with a space so that the
stemmer will see two separate words. The HTML
feature being examined is then passed through the
stemmer in its entirety and then compared to the
stemmed search query. The comparison made
between feature and query text is explained in the
next section.
3.4 Determining Relevant Images
When comparing query text to text contained in an
HTML feature, one of two relevant situations exists.
 The HTML feature exists in relation to the
image and contains text matching the query
text.
 The HTML feature does not exist in relation to
the image or does not contain any query text.
A value is assigned to both situations: 1 and 0
respectively. These values are used in the following
equation along with the coefficients seen in Table
3.1 to generate a relevance probability using an
equation developed by forward stepwise logistical
regression:
P
event
=
e
z
1 + e
z
=
1
1 + e
−z
where Z = B
0
+ B
1
X
1
+ B
2
X
2
+ ... + B
k
X
k
(3.1)
If the equation produces a probability that meets a
specified threshold, the image will be added to a list
of other relevant images. Thresholds of 0.7 and 0.5
were used for searches on the Web and on the local
database respectively. The threshold was set lower
for a local database search due to a relatively small
number of images present. These thresholds were
chosen arbitrarily through trial and error. It should
be noted that images that have a height or width less
than 100 pixels or have an aspect ratio greater than 2
are determined irrelevant. These characteristics are
common for decorative images or advertisements.
3.5 Image Presentation
When all of the Web pages have been processed, the
list of image URLs is sent back to the search page.
Images are sorted and displayed according to decrea-
Table 3.1: Relevance model coefficients computed by
logistical regression.
Feature
B
Constant -2.317
Image Filename 1.886
Page Title 1.092
Page Filename .867
ALT Attribute 1.076
Image Path 1.060
Page Path -.787
Cell Below .709
Meta Description 1.092
Cell Above .664
Other Body Text -.222
Anchor Text 2.023
Cell Right .331
Cell Left .370
-sing relevance. Images are displayed in 3 rows of 5
images each. Images are scaled according to a set
width to create a uniform table width. Each image is
a link to the original image. When more than 15
images are returned, links to navigate image pages
are displayed below the image table.
4 RESULTS
Using the application described in the previous
section, data was gathered to examine the
effectiveness of stemming in text-based image
retrieval for images on the Web. The 24 queries used
to produce the local dataset were queried against the
dataset with the addition of stemming. Additionally,
images produced by 10 queries using pages gathered
by Google were examined with and without
stemming. When stemming was implemented in a
search, it was performed on a given HTML feature
only when no match was found for that HTML
feature without stemming.
This data was analyzed using two techniques:
precision and unique relevance recall. Both of these
metrics will be discussed in this section. The results
for both queries on the local dataset and queries on
the Web will be presented, and an analysis of issues
relating to stemming will be given.
4.1 Analysis Models
To evaluate our application, we used two metrics
called precision and unique relevance recall.
Precision is a measure of the accuracy of a search. It
measures the percentage of relevant results among
all results provided. Unique relevance recall (URR)
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is a metric used to compare two or more search
algorithms (Kowalski, 1997). It measures the
number of unique relevant results produced by one
algorithm and not produced by other algorithms.
Because we are exploring the effectiveness of
stemming, we compare the results produced by a
search without stemming with those produced by a
search with stemming. The equations for precision
and unique relevance recall are:
Image relevance was determined by three trained
image raters. If at least two of the three raters
deemed an image relevant, the image was
determined to be relevant. The goal of this analysis
is simply to test our hypothesis that stemming is a
useful addition to a text-based image retrieval
system for use on the Web.
4.2 Local Image Search Results
Each of the 24 queries used to generate the local
dataset were rerun on the local dataset both with and
without stemming. In both cases the relevant images
were noted and search precision calculated. The
images returned using both algorithms were
compared, and uniquely relevant images were
identified. In 21 of the 24 queries, the results were
identical between the two searches. In the remaining
3 queries, stemming produced all of the images
returned by the search without stemming as well as a
few additional images. Unlike the other 21 queries,
these 3 queries contain terms that lend themselves
well to stemming. In 2 of these 3 queries, the
additional images returned by stemming were
relevant to the search query. The results from the
local data search can be seen in Table 4.1.
4.3 Web Image Search Results
To examine the effectiveness of stemming on text-
based image retrieval on the Web, 10 queries were
used. These queries were selected for their perceived
appropriateness for stemming. They are not meant to
be a representative set of queries for the application
of Web image retrieval. These 10 queries were
submitted both with and without stemming. The
resulting relevant images were identified and the
search precisions recorded. In 8 of the 10 queries,
the result sets where stemming was implemented
contained all of the images produced without
stemming along with additional relevant images. In
the remaining 2 queries, stemming allowed a match
in a feature determined to lower the relevance
according to the image relevance equation. The
average precision among the 10 queries was 82.5%
without stemming and 84.5% with stemming. The
average unique relevance recall with stemming was
0.5% and 27.7% with stemming. The results from
the Web image search can be seen in Table 4.2.
4.4 Discussion
This research shows that stemming is useful to a
certain extent in text-based image retrieval for
obtaining additional relevant results. It also shows
that stemming a given HTML feature only when a
match is not found without stemming allows
stemming to be implemented in a manner that is
unlikely to exclude results that would have been
returned had stemming not been implemented.
On the queries ran against the local data set, a
small improvement was seen with the addition of
stemming. Of the 24 queries, only 3 produced
additional images with stemming. Of these 3
queries, 2 returned additional relevant images. This
is likely due to the nature of the queries themselves.
A total of 15 of the queries were proper names of
people, places, or monuments. Because stemming is
applied in an effort to create matches among
multiple word forms, it follows that queries of
proper names would not benefit from stemming. For
the same reason, the 3 queries of holidays, "new
year", "thanks giving", and "halloween" are unlikely
to benefit from stemming. Of the remaining 6
queries, 2 contained the result set that was improved
by stemming. These queries were "burning house"
and "raining". Both of these queries contain a
present participle verb ending in "ing". Stemming in
these situations not only returned additional relevant
images, thus raising the URR, but also improved the
precision for the search. The one query that
produced additional results, none of which were
relevant, was the query of "thanks giving". Due to
the word "giving" in this query, it is not surprising
that stemming produced additional results. However,
"thanks giving" is typically written as a single word
"thanksgivng", which may have some effect on the
results for this query. These results illustrate the fact
that stemming is not necessarily beneficial for all
types of search queries.
For the result set produced by image search on
the Web, stemming proved to be useful. The average
URR with stemming was 27.7% while increasing the
precision by 2.0% from 82.5% to
Precision =
Number _ Relevant _ Retrieved
Total _ Number _ Retrieved
(4.1)
URR =
Number _ Unique _ Relevant
Number _ Relevant
(4.2)
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Table 4.1: Local data search results.
ID
Query Precision w/o Stemming
Precision
w/Stemming
URR w/o
Stemming
URR
w/Stemming
1 Britney Spears 0.903 0.903 0 0
2 George Bush 0.623 0.623 0 0
3 Bomb Explode 0.5 0.5 0 0
4 Eiffel Tower 0.703 0.703 0 0
5 Statue Liberty 0.836 0.836 0 0
6 Vietnam Memorial 0.511 0.511 0 0
7 Volcano Erupt 0.583 0.583 0 0
8 Raining 0.286 0.333 0 0.333
9 Bill Gates 0.698 0.698 0 0
10 Happy Child 0.133 0.133 0 0
11 Burning House 0.750 0.800 0 0.25
12 Sad Woman 1.00 1.00 0 0
13 New Year 0.367 0.367 0 0
14 Halloween 0.773 0.773 0 0
15 Thanks Giving 0.200 0.182 0 0
16 John White 0.911 0.911 0 0
17 Michael Brown 0.800 0.800 0 0
18 William Black 0.750 0.750 0 0
19 New York 0.722 0.722 0 0
20 Michigan Lake 0.510 0.510 0 0
21 Yellowstone Park 0.836 0.836 0 0
22 Burlington Vermont 0.640 0.640 0 0
23 Spokane 0.700 0.700 0 0
24 Haw River 0.833 0.833 0 0
Table 4.2: Web image search results.
ID Query Precision w/o Stemming
Precision
w/Stemming
URR w/o
Stemming
URR
w/Stemming
1 Downhill Skiing 0.828 0.861 0.034 0.222
2 Crying Babies 0.933 0.882 0 0.118
3 Whitewater Rafting 0.841 0.877 0 0.228
4 Sculptures 0.943 0.933 0.014 0.387
5 Mountain Climbing 0.857 0.868 0 0.079
6 Puppies 0.941 0.918 0 0.265
7 Kittens 1.000 1.000 0 0.412
8 Candied Apples 0.667 0.714 0 0.429
9 French Fries 0.742 0.765 0 0.088
10 Runners 0.500 0.636 0 0.545
84.5%. The queries chosen were selected because of
their perceived appropriateness for stemming. Each
of the 10 queries used in the Web searches contained
at least one plural noun, present tense verb, present
participle verb, or past tense verb. Therefore, the
results produced from stemming may more
accurately apply to queries containing one or more
of these word forms. Based on the results produced
from the local Web page dataset, stemming is not
likely to improve queries without these word forms.
Another important observation is that stemming,
implemented only when a match was not found
using traditional matching alone, excluded very few
images returned from a search using only traditional
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229

matching. When implemented on all features
regardless of a match without it, stemming excluded
many more images produced by the search without
stemming. A possible reason for this is due to a
common file naming convention for images on the
Web. Images tend to have names like
"babycrying.jpg" or "puppies02.gif" where either
multiple words are combined or a sequence number
is appended to the text describing the image.
Because the image filename is one of the greatest
contributors to image relevance according to our
relevance model, a lack of a match in this situation
could make the difference between an image being
in the result set or being excluded. While a
traditional comparison between query text and
image filename may find a match in a similar
situation, stemming may not find a match. This issue
may also arise in the page filename, image path,
page path, and ALT attribute, as it is not uncommon
for the ALT attribute to be given a value equal to the
image filename.
5 CONCLUSIONS
With the availability of nearly any desirable image
on the Web, effective tools for searching for images
are needed. Because of the incredibly large size of
the Web as well as its constantly changing nature,
new techniques need to be explored and
implemented. While some commercial image search
engines exist, the techniques used in these search
engines are mostly unknown.
Stemming was most beneficial for queries that
contained at least one plural noun, present tense
verb, present participle verb, or past tense verb.
Image searches with stemming on queries containing
at least one of these word forms returned additional
relevant images compared to searches without
stemming while also slightly increasing precision.
Additionally, by stemming an HTML feature only
when a query text match was not found without
stemming, we were able to obtain nearly all images
returned from a search without stemming.
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