=Paper= {{Paper |id=Vol-1177/CLEF2011wn-ImageCLEF-ZellhoeferEt2011 |storemode=property |title=BTU DBIS' Multimodal Wikipedia Retrieval Runs at ImageCLEF 2011 |pdfUrl=https://ceur-ws.org/Vol-1177/CLEF2011wn-ImageCLEF-ZellhoeferEt2011.pdf |volume=Vol-1177 }} ==BTU DBIS' Multimodal Wikipedia Retrieval Runs at ImageCLEF 2011== https://ceur-ws.org/Vol-1177/CLEF2011wn-ImageCLEF-ZellhoeferEt2011.pdf

BTU DBIS’ Multimodal Wikipedia Retrieval
Runs at ImageCLEF 2011

David Zellhöfer and Thomas Böttcher

Brandenburg Technical University, Database and Information Systems Group,
Walther-Pauer-Str. 1, 03046 Cottbus
david.zellhoefer@tu-cottbus.de

Abstract. In this work, we summarize the results of our first partici-
pation in the Wikipedia Retrieval task. For our experiments, we rely on
a cognitively motivated IR model: the principle of polyrepresentation.
The principle’s core hypothesis is that a document is defined by different
representations such as low-level features, or textual content that can be
combined in a structured manner reflecting the user’s information need.
For our first participation, we used mono-lingual English retrieval in com-
bination with global low-level features without further user interaction
or query modification techniques.
Our best NOFB reached rank 64 or rank 13 of the mono-lingual English
runs. This result is promising as we have not used structural informa-
tion about the documents. Additionally, our findings are indicating the
correctness of the polyrepresentative hypothesis for multimodal retrieval.

Keywords: Content-Based Image Retrieval, Information Retrieval, Re-
sult Fusion, IR Model, Polyrepresentation, Experiments

1 Introduction

The Database and Information Systems Group of the Brandenburg Technical
University (DBIS) is participating for the first time in ImageCLEF’s Wikipedia
Retrieval task [17]. Hence, our first submitted runs are focussing at gaining expe-
rience with the handling of the task and at testing recently published theoretical
results.
Over the past years, DBIS has worked on establishing a new query language,
the commuting quantum query language (CQQL) [14], which is capable of com-
bining similarity predicates as found in information retrieval (IR) as well as
relational predicates common in databases (DB). In addition, CQQL is an ex-
tension of the relational domain calculus, i.e., it can be directly executed within
a relational DB system [8]. To combine both data access paradigms, CQQL relies
on the mathematical foundations of quantum mechanics and logic. For the scope
of this paper, we will not emphasize the theoretical implications of the quantum
theoretical background. However, the relation of CQQL to fuzzy logic [19] can
be found in [15]. Its relation to probabilistic IR models is discussed in [21, 25].
2

Recently, the correlation between the principle of polyrepresentation [6] and
quantum mechanics-inspired IR models has been discussed in the community
[21, 5, 24]. Being based on van Rijsbergen’s basal work [11], two approaches are
researching a combination of the principle of polyrepresentation with ideas de-
rived from quantum mechanics [10, 23]. Only the latter approach is dealing with
multimodal retrieval explicitly. Thus, it will be used as the theoretical IR model
for the remainder of this paper.

2 Retrieval Model
The discussed IR model is using CQQL to implement the findings of the cog-
nitively motivated principle of polyrepresentation. The main hypothesis of the
principle is that a document is defined by various functionally and cognitively
different representations [6]. “Functionally different representations are the title
of a document, the actual textual content, or an abstract that has been created
by a sole actor, e.g., the author. Cognitively different representations denote
such representations that have been created by another actor such as a retrieval
model1 , summaries written by a librarian, or former related INs. Hence, a com-
bination of different IR systems that describe the same document (e.g. examined
in [7]) can be regarded polyrepresentative as well.” [25]
If one combines these representations into a so-called “cognitive overlap”
(CO), documents that are contained within this overlap are likely to be relevant
w.r.t. a user’s information need (IN). As a consequence, it is assumed that this
combination of representation can compensate the insecurity of relevance assess-
ment of retrieved documents [6]. The utility of the principle of polyrepresentation
has been shown for (textual) IR [16, 7]. In addition, both aforementioned works
address actual means of construction of a cognitive overlap. As a result, it could
be shown that highly structured queries2 modeling a CO lead to higher precision
in direct comparison to unstructured queries such a bag-of-words.
As said before, our research relies on CQQL to reflect the principle of polyrep-
resentation in a query language. We believe that the principle provides a sound
theoretical framework for dealing with the various representations that are in-
herent within a multimedia document. As a cognitively motivated model, it does
not stop here. Furthermore, it can include additional information that is impor-
tant during a search task, such as the user’s context. The representations of a
document can have different origins such as copyright information stored in a
DB, text annotations or low-level features like a color histogram. This data has
to be accessed and combined in a consistent manner in order to retrieve mul-
timodal data. CQQL provides means to query both IR and DB systems while
maintaining their semantics. For instance, representations from a relational DB
can be restricted in Boolean fashion to evaluate only against true or false (i.e. 1
1
Ingwersen and Järvelin regard an IR system as a peer actor because it assesses the
relevance of a document on basis of different representations. Thus, it participates
as well in the search.
2
A query combining Boolean connectors to combine parts of a query.
3

or 0). In contrast, representations originating from an IR system maintain their
similarity semantics in an interval of [0; 1] (for details see [14]).
To simplify, CQQL can be considered a query language dealing with proba-
bilities that is consistent with the laws of the Boolean algebra. The probabilities
denote how likely it is that a certain document’s representation is relevant to the
CO and therefore the user’s IN. In the next section, we will sketch the arithmetic
evaluation of CQQL as it is necessary for the understanding of this paper.

2.1 Evaluation of CQQL

Given that fϕ (d) is the evaluation of a document d w.r.t. a CO modeled by a
CQQL query. To model a CO, various representations ϕ can be linked in an
arbitrary manner using the conjunction (1), disjunction (2), or negation (3). If
ϕ is atomic, fϕ (d) can be directly evaluated yielding a value out of the interval
[0; 1] As stated before, the actual value of a representation can be calculated by
a similarity measure or a Boolean evaluation carried out by a DB system or the
like.
After a necessary syntactical normalization step [22], the evaluation of a
CQQL query modeling the CO is performed by recursively applying the suc-
ceeding formulas until the atomic base case is reached:

fϕ1 ∧ϕ2 (d) = fϕ1 (d) ∗ fϕ2 (d) (1)

fϕ1 ∨ϕ2 (d) = fϕ1 (d) + fϕ2 (d) − (fϕ1 (d) ∧ fϕ2 (d)) (2)

f¬ϕ (d) = 1 − fϕ (d) (3)
An example of an arithmetic evaluation of a sample query is given in Sec. 3.1.
The result of an evaluation of a document d yields the probability of relevance
of d w.r.t. the given CO. This probability value is then used for the ranking of
the result list of documents.

2.2 Weighting in CQQL

In order to steer the influence of certain representation onto the CO, CQQL has
been extended with a weighting scheme [13]. This weighting scheme can be used
for relevance feedback (RF) during the retrieval process. Weighting is a crucial
part of our machine-based learning supported user interaction model discussed
in [22, 25]. Although an evaluation of RF for multimodal retrieval is not in the
scope of this paper, we will outline how weights are embedded in a CQQL query
because they will occur with fixed settings in Sec. 3 as part of the experiments.
Eq. 4 denotes a weighted conjunction, whereas Eq. 5 states a weighted dis-
junction. A weight θi is directly associated with a logical connector and steers
the influence of a representation ϕi on the evaluation. To evaluate a weighted
4

CQQL query, the weights are syntactically replaced by constant values according
to the following rules:

ϕ1 ∧θ1 ,θ2 ϕ2 (ϕ1 ∨ ¬θ1 ) ∧ (ϕ2 ∨ ¬θ2 ) (4)
ϕ1 ∨θ1 ,θ2 ϕ2 (ϕ1 ∧ θ1 ) ∨ (ϕ2 ∧ θ2 ) (5)

3 Experiments

Recently conducted experiments provided promising results about the utility of
the principle of polyrepresentation for the content-based image retrieval (CBIR)
domain [25]. The same findings apply to the textual IR domain [16, 7]. As a
consequence, our experiments are focussing on the combination of both worlds.
Yet, we are not researching representations derived from image metadata or
the like. For the scope of this paper, experiments have been conducted only
on textual information and low-level features alone. In addition, our first runs
do not incorporate any user modification of the query or interaction during the
retrieval process such as relevance feedback. For the sake of simplicity, we do not
use other languages than English during the experimental retrieval. Although all
nodes within the provided XML documents were indexed, structural information
such as “title” has not been used explicitly.
In order to obtain the different representations for each document, we used
the Indri 5 IR engine from the Lemur project3 . All text retrieval has been carried
out by this engine. The global low-level features of the images involved in the
task were extracted by LIRE 0.8 [9]. Tab. 2 lists all available low-level features
that are mostly part of the MPEG-7 standard besides CEDD (also provided
during the Wikipedia Retrieval task [17]) and FCTH. Regarding the principle
of polyrepresentation, the LIRE features are different functional representations
of a document while Indri provides a cognitively different one. To address the
fact that multiple query-by-example (QBE) image documents were available
for each query, we have created averaged representations to keep the evaluation
simple (see Sec. 4 for details). To obtain averaged representations, we calculated a
similarity value for each representation of each QBE image and a given document
of the collection. Then, we calculated the arithmetic mean over all QBE image
similarities for a given document.
Before our ImageCLEF-related experiments, we have analyzed the perfor-
mance of different queries that involved LIRE-based representations alone (see
Tab. 1). In order to obtain meaningful results, we tested various features and fea-
ture combinations with the following well-known collections “Wang” [18], “UCID
v2” [12], and “Caltech 101” [4]. Additionally, we have used the “Bali” collection
[25] as a representative of personal holiday photographies. Fig. 1 illustrates some
of the findings. For the scope of this paper it is notable that the highly structured
queries Q10 and Q9 perform almost as well as QAN D or QM AX while relying
on about half of the available features. The features used in Q10 and Q9 have
3
http://www.lemurproject.org/
5

been chosen because of their performance when used on their own and to mirror
assumptions about the utility of a combination of structural and color-based
features as stated in [3, 2].
To evaluate the principle of polyrepresentation’s utility for multimodal re-
trieval, we used two different strategies to model the CO and to assess the
relevance of a document w.r.t. a given query.

Table 1. Analyzed Sample Queries (Excerpt) from [25]

Name Query
COLORHIST COLORHISTsim
QMAX maxsim (SCALABLECOLORsim , COLORLAYOUTsim ,
EDGEHISTOGRAMsim , TAMURAsim , COLORHISTOGRAMsim ,
CEDD
V sim , GABORsim , AUTOCOL.CORRELOGRAMsim , FCTHsim )
QAND θi (SCALABLECOLOR sim , COLORLAYOUTsim ,
EDGEHISTOGRAMsim , TAMURAsim , COLORHISTOGRAMsim
, CEDDsim , GABORsim , AUTOCOL.CORRELOGRAMsim , FCTHsim )
Q9 (CEDDsim ∨θ1 ,θ2 FCTHsim ) ∧ (COLORLAYOUTsim ∨ TAMURAsim )
Q10 (CEDDsim ∨θ1 ,θ2 FCTHsim ) ∧ (COLORLAYOUTsim ∨ (TAMURAsim ∧
EDGEHISTOGRAMsim ))
Q13 COLORLAYOUTsim ∧ TAMURAsim
Weights (θi ) are initially set to 0.5. XYsim denotes similarity of a repre-
sentation to the QBE document.

3.1 CQQL-based Fusion

Our first approach used the direct evaluation of CQQL queries. The experiment
subdivides into three parts that are related to the used modalities, features, and
structural characteristics of the query itself. If weights were present in a query,
they were set to 0.5.

Group 1 consists of the queries Eq. 6, Eq. 7, and Eq. 8. All have in common
that the LIRE-based part of the CO model is based on Q10 (see Sec. 3). For
the discussed runs, COLORLAY OU T has been removed. This is due to the
fact that this feature could not be extracted for a number of image documents.
See Tab. 2 for a list of all missing features. This problem has been caused by
corrupted ICC profiles within the ImageCLEF image document corpus. These
corruptions resulted in a crash of Java’s ImageIO during file access.
x
The textual part of the CO (denoted as IN DRIsim , whereas x stands for
the query type) has been calculated by Indri using different search strategies.
All Indri queries used Krovetz stemming and case normalization4 . The English
4
See http://lemur.sourceforge.net/indri/IndriIndexer.html for the standard settings.
6

query text defined by the task [17] served directly as query terms. The differences
of the Indri query formulation are as follows.

ordered window
Q71−72 : IN DRIsim ∧ ((CEDDsim ∨θ1 ,θ2 F CT Hsim )∧ (6)
(T AM U RAsim ∧ EDGEHIST OGRAMsim ))
Here, the English query text has been input directly including the information
that the query terms in a document have to appear ordered with at most 4 terms
between them using Indri’s ordered window operator. A sample Indri query for
the text “close up of trees” would look like this: #combine(close up of trees
#5(close up of trees)).
In combination with the LIRE-based representations this results in the fol-
lowing arithmetic evaluation given in pseudo code:

q10 = (1 − ((1 − (cedd ∗ (1 − (1 − θ1 )))) ∗ (1 − (f cth ∗ (1 − (1 − θ2 ))))));
relevance = indri ∗ q10;

ordered window+SW
Q73 : IN DRIsim ∧ ((CEDDsim ∨θ1 ,θ2 F CT Hsim )∧ (7)
(T AM U RAsim ∧ EDGEHIST OGRAMsim ))
This query is equal to 6 but uses stop word elimination.

plain combine
Q79 : IN DRIsim ∧ ((CEDDsim ∨θ1 ,θ2 F CT Hsim )∧ (8)
(T AM U RAsim ∧ EDGEHIST OGRAMsim ))
This query is equal to 6 but drops the ordered window operator resulting in
queries such as #combine(close up of trees).

Group 2 consists of one query that uses the modified version of Q10 alone in
order to measure the “raw” low-level feature performance.
Q110 : (CEDDsim ∨θ1 ,θ2 F CT Hsim )∧ (9)
(T AM U RAsim ∧ EDGEHIST OGRAMsim )

Group 3 contains COs that have not been sent in before. Because of the missing
features for some documents (see Tab. 2), we have investigated the influence of
such incomplete information on the retrieval performance of our approach.
^
ordered window
Q500 : IN DRIsim ∧ ( (All LIRE f eatures)) (10)
θi

For the first query, we used the best performing Indri query in a conjunction
with a weighted conjunction of all 9 LIRE features.
^
ordered window
Q600 : (IN DRIsim , All LIRE f eatures) (11)
θi
7

The second examined CO is modeled by a weighted conjunction of all LIRE
features in addition to the representation provided by Indri. The difference to
Eq. 10 is that the textual representation does not have more impact on the
result. Here, it is a mere peer amongst all other representations.

Table 2. Missing features after LIRE 0.8 extraction

Feature Absolute Percentage
COLORHISTOGRAM 28339 11.90%
SCALABLECOLOR 10116 4.26%
AUTOCOLORCORRELOGRAM 10116 4.26%
COLORLAYOUT 10065 4.23%
EDGEHISTOGRAM 1 <0.01%
CEDD 1 <0.01%
FCTH 1 <0.01%
TAMURA 36 <0.01%
GABOR 36 <0.01%

3.2 Pre-Filtering Approach

For our second approach, we have examined a pre-filtering approach that is
closely related to [1]. First, we have calculated the relevance of each document
according to Indri’s ordered window operator including stop word elimination
as described for Eq. 7. Second, we have calculated the average similarity for
each image document. That is, we have computed each LIRE feature similarity.
Then, all similarity values have been averaged to obtain an averaged similarity
value. Furthermore, we averaged the result over all QBE documents for each
query. The result list of the image retrieval was then reduced to the top 5000
documents.
To combine both result lists, we regarded the Indri list as a pre-filter. In other
words, we only considered documents that are present in this list as potentially
relevant. In a next step, we obtained the relevance score of a document d in the
Indri list as the document’s base score. If document d was also present in the
low-level feature-based result list, we improved its relevance score by 0.15. This
reflects the principle of polyrepresentation’s hypothesis that two cognitively dif-
ferent representations “pointing” at one document are more likely to be relevant.
This approach leaves the proposed usage of CQQL and is merely based on
heuristics. The choice of a boosting factor of 0.15 is based on experiments car-
ried out with the ImageCLEF 2010 collection and ground-truth alone. A formal
justification cannot be given. This approach is listed in Tab. 3 as run #64.
8

3.3 Strategy Comparison

In compliance with the findings from [1] our pre-filtering approach performed
best. A direct comparison to the other contributors is difficult as not enough
information about their runs is available at the present moment. Nevertheless,
we compare our approaches with the best performing, automatic English-only
textual run #45. Tab. 3 gives an overview of our overall performance. We assume
that run #45 uses some kind of optimized query formulation strategy while we
are using a fairly naive approach towards the textual retrieval part. That is,
we basically hand the query text directly to the Indri IR engine and neglect
additional structural information, e.g., if a term is present in a document’s title.
Still, our pre-filtering approach #64 reaches 84,39% MAP performance of the
best textual run #45, whereas the pure CQQL runs are in the range of 80.58%
to 75.39%. As expected, the plain visual run #110 performs very bad (1.31%
MAP of #45). This behavior is in accordance with the result trend from former
ImageCLEF Wikipedia Retrieval tasks.
An interesting finding from [25] could be duplicated within the Wikipedia
Retrieval task. It seems that there is evidence that a structured combination of
functionally different representations (here: low-level features) can compensate a
large amount of representations or even surpass it in terms of retrieval quality. In
our scenario, run #500 makes use of 9 CBIR-based representation while run #64
and #71-79 use only 4, i.e., 44 % of the available features. This is strengthening
the polyrepresentative hypothesis as well as it might be a fruitful area of future
research because the actual calculation of such representation can be a costly
operation – especially in the field of multimedia retrieval. Concluding from the
results of [25], we did expect a slightly better performance for queries involving
all features (see run #500) in comparison to, e.g., #71. In our opinion, the
presence of missing representations lead to the inverse effect. Hence, it cannot
be generalized that a structured combination of representations will outperform
an unstructured query in every case. This leaves room for further research.
Run #600 (without an emphasis on textual representations) results in a
MAP performance of 19.38% clearly showing that the main contribution of the
retrieval quality is derived from textual representations.
Regarding fusion strategies, the findings from the textual domain as stated
in [16, 7] that highly structured queries alone will improve the retrieval quality
cannot be justified for the multimodal retrieval domain for now. Our current
results indicate that a cleverly chosen fusion strategy itself is contributing to the
overall retrieval quality of a multimodal retrieval system.

4 Conclusion and Future Work

Regarding our low experience with the Wikipedia Retrieval task, we are sat-
isfied with our results. Our best run is place at rank 64, i.e., rank 13 of the
mono-lingual English runs. If we consider NOFB runs only, we get rank 4 being
the only multimodal approach in this mono-lingual ranking. In accordance with
9

Table 3. DBIS’ runs in comparison to the best automatic English-only textual run

Run Modality Language Feedback MAP P@10 Percent of Best Run Performance
45 Textual English None 0.2601 0.456 Best Eng.-only Text Run (UNED)
64 Mixed English None 0.2195 0.418 MAP: 84.39%; P@10: 91,67%
71 Mixed English None 0.2096 0.408 MAP: 80.58%; P@10: 89.47%
72 Mixed English None 0.2096 0.408
73 Mixed English None 0.2096 0.408
79 Mixed English None 0.1961 0.382 MAP: 75.39%; P@10: 83.77%
500 Mixed English None 0.2061 0.400 MAP: 79.24%; P@10: 87.72%
600 Mixed English None 0.0504 0.1660 MAP: 19.38%; P@10: 36.40%
110 Visual N/A None 0.0034 0.020 MAP: 1.31%; P@10: 4.39%

our findings about the contribution of textual representations, we assume that
the poor performance in comparison to the text-only runs is due to our naive
approach towards textual IR. Hence, we would really appreciate the cooperation
with other working groups having a strong textual IR experience. In future, we
will incorporate the usage of structured information within the XML documents
as it is already supported by the CQQL-based query model.
Another criticism of our approach is the over-simplification of the principle
of polyrepresentation for the CBIR part of our work. The averaging of represen-
tations in order to deal with multiple QBE documents within one query removed
important semantics from the query. To model the CO in a better way, each QBE
document has to be used as an additional cognitively different representation of
the query. We assume that this might improve the retrieval quality because it is
likely to reflect the user’s subjective IN more complete. In combination with our
RF approach [22, 20], this might improve the overlap between the user’s cogni-
tive structures – e.g. expressed by different images with different contributions
to the user’s IN– and the actual query.
Another field for further research is the dealing with missing representations.
This is an important issue as it cannot be assumed that all queried representa-
tions are always present. For instance, this problem is likely to re-appear when
distributed IR systems are used or cross-media retrieval is concerned.
Although it is somewhat out of the scope of system-centric initiatives such as
ImageCLEF, we will continue our research on integrating contextual information
about the user.

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workshop on Ph.D. students in information and knowledge management. PIKM
’11, ACM (2011) (to appear)
12

1
RF AND
0.95 Pseudo RF
0.9 AVG
0.85 AND
0.8 Q10
ComboOR
0.75
Q9
0.7
Q13
0.65 Colorhist
0.6 RF Q10
0.55 MAX
0.5 RF AND
Pseudo RF
0.45
AVG
0.4
AND
0.35 Q10
0.3 ComboOR
0.25 Q9
0.2 RF Q10
MAX
0.15
Colorhist
0.1
0.05
0
P at 5 P at 10 P at 15 P at 20 P at 30

1
RF AND
0.95 Pseudo RF
0.9 AVG
0.85 AND
0.8 Q10
ComboOR
0.75
Q9
0.7
Q13
0.65 Colorhist
0.6 MAX
0.55 RF Q10
0.5 RF AND
Pseudo RF
0.45
AVG
0.4
AND
0.35 Q10
0.3 ComboOR
0.25 RF Q10
0.2 Q9
Q13
0.15
Colorhist
0.1
MAX
0.05
0
P at 5 P at 10 P at 15 P at 20 P at 30

Fig. 1. Comparison of P@X w.r.t. Different Queries, Relevance Feedback Strategies
and Document Collections (left: Wang [brown, solid], Caltech 101 [blue, dashed]; right:
Bali [purple, solid], UCID [red, dashed]) [25]