DEIM Forum 2013 A3-4

探索的検索ためのテキスト景観
趙夢†

大島 裕明†

田中 克己†† 京都大学大学院情報学研究科社会情報学専攻 〒 606–8501 京都府京都市左京区吉田本町
E-mail: †{zhao,ohshima,tanaka}@dl.kuis.kyoto-u.ac.jp

Text Landscape for Exploratory SearchMeng ZHAO† , Hiroaki OHSHIMA† , and Katsumi TANAKA†† Department of Social Informatics, Graduate School of Informatics, Kyoto University
Yoshida Honmachi, Kyoto, 606–8501 Japan
E-mail: †{zhao,ohshima,tanaka}@dl.kuis.kyoto-u.ac.jp
Abstract With the help of state-of-the-art information retrieval technologies, most relevant documents come tothe very top of ranked list in the reponse of user’s query. They are well performed for navigational search. However,for informational search, or more general case, exploratory search, whose search process is iterative, multi-tactical,
spanning multiple queries or search sessions, they don’t seem to be eﬀective enough. One reason is that ”relevance”
is considered to be the most crucial feature rather than“relatedness” which contains more, such as rival information.
In this work, we introduce a method to present the whole landscape view of related information according to auser-indicated document in order to show a complete knowledge environment.
Key words text landscape, exploratory search, text similarity, text adjacency

a search query to Google, if the input query is fortunately

1. Introduction

well-formed to retrieve the target, it will help us reach theWith the help of state-of-the-art information retrieval tech-target within a short time. However, well-formed queries arenologies, most relevant documents come to the very top of

not always easily created. In this case, how we can achieve

the ranked list in the response of user’s query. For example,

our target or potential goal, is an important issue. Nowdays,

Fig.1 shows the top search result under the query “iphone 5”query suggestion is widely used for guidance in the infor-

returned by Google（注 1）. If our goal is to ﬁnd the oﬃcial web

mation space. We think it is an indirect guidance since it（注 2）

only oﬀers users possible search directions. Still the “iphone

gives us the exactly right answer. In another case, if our goal5” example. Google recommends queries such as “iphone

is to learn something about “iphone 5”, the ﬁrst page may

5 cases”, “iphone 5 reviews”, “iphone 5 accessories” and so

be one of our choices, but obviously it is not enough. It is

forth. But it does not show user feasible relation between

likely that we will click and read several pages to satisfy this

“iphone 5 cases” and “iphone 5 accessories”, while user’s po-

information need. Or even sometimes we would reformulatetential goal is hidden in such relations.

site of “iphone 5”, then the ﬁrst page of Apple iphone 5

our query in order to get a better result. It is conceivable

Why do searches bog down in those cases? The key rea-that this kind of search covers several search session. As ason is the state-of-the-art information retrieval technologies

result, it will cost user much time to complete a whole search

do not show searchers the “surrounding information” aboutprocess. And most of the time is spent to read and eliminate

the query topic. Here so-called “surrounding information” isirrelevant information. Another example is when we issuereferred to as related documents of the query topic, containing but not limited to rival documents, documents of super-（注 1）：http://www.google.com/（注 2）：http://www.apple.com/iphone/ordinate concepts. By showing searchers the “surrounding
information” and their relations in a more organized way, wepercents of queries belong to informational, 30 percents belong to transactional, and only 20 percents belong to navigational. In [7], Rose and Levinson proposed a slightly different classiﬁcation method which is based on why people
are searching rather than how they search and what they
are searching for. In their framework, “resource” category
replaces Broder’s notion of transactional queries. Both [6]and [7] reach an agreement about what “navigational” queryis. However, Rose and Levinson believed in their “informational” queries, there include some further interactions, since
the user’s goal is to obtain information about the query topic.
The completely diﬀerent one, “resource” queries are intended
to get something, such as download a song, view a video. In
their experiment, it was pointed out that around 60 percent
of queries are “informational”. This leads to the conclusion
over 75 percents of user queries are non-navigational ones.
Exploratory search is deﬁned to be used to describe
図 1 A search example by Google.

an information-seeking problem context and informationseeking processes in [13]. Especially, the information-seeking

believe not only the satisfaction of searcher experience but
also the eﬃciency to locate search goal will be substantially
improved.
In this paper, we propose a novel kind of information
search that given a web page of search result of a query, a“text landscape” (See detials in Section 3.) of this query will
be returned. In general, “text landscape” provides a wholeknowledge environment of the query topic, composed mainlyby the “surrounding information”. In order to discover documents or web pages which belongs to the same landscape,
we incorporate adjacency into our scheme.
The remainder of the paper is organized as follows: Section
2. introduces some related works; in Section 3., the deﬁnition of “text landscape” is stated; in Section 4., the basic
idea and the problems are addressed; Section 5. explains our
proposed method PanoramaRank for text landscape; ﬁnally,
conclusions and possible directions for future work are statedin Section 6..

2. Related Work
Some researches are devoted to classify web searches. In[6], Broder stated there are three types of web queries:“navigational”, “informational” and “transactional”. Usually there is only one right result in the case of navigational
queries. User makes an eﬀort to ﬁnd a particular web page
which has in his mind before doing a search. While in thecase of informational queries, user tries to ﬁnd information
about a topic, but with no further interaction predicted.
Transactional searches are intended to reach a site including some further interactions. Broder found that about 48

problem context is “open-ended, persistent, and multifaceted” and processes are “opportunistic, iterative, andmulti-tactical”. It subtly diﬀeres from information retrieval(IR): in IR, the search target is usually known before the user
query is issued. In contrast, in information seeking, it is uncertain about the existance of the information being sought
and the ability that the searcher can ﬁnd it. In the worst
case, searcheres are even unsure about their goals. Take thesearch process of iterative search and exporatory search for
instance. In Fig.2(a), as the search target is known, our task
is to create the well-formed query that will retrieve it so thatduring the search process, query is reformulated to make thesearch result close to the target. In contrast, when we seeksome information of our interest, although we have a certain
information need, our search is likely to be varied. For example, in Fig.2(b), obviously the ﬁrst-time search result is notwhat we need, so query is reformulated and the second-timesearch is started. At this time, it seems to be relevant to
our information need so that the following searches tend toconverge. However, after we understand this ﬁeld of information to some extent, we get to realize that it is not what
we are seeking for. So a new search journey is started. Thatmeans in exploratory search, with the deepening of understanding, the information need becomes clear and deﬁnite.
If the whole knowledage environment about a query topic is
well organized in advance, it is possible for users to realize
their ambiguous information need at the early stage so thatthe number of searches can be reduced substantially.
“Neighbor information”, a similar concept of our “surrounding information”, is eﬀectively used in some studies.
Castillo et al. incorporated neighbor information to detect

a better classiﬁcation result. In [12], Wan and Xiao statedan approach to improve single document keyphrase extraction. They also exploited neighbor information. But in theirwork, the neighbor documents are obtained by using document similarity search techniques, which is a content-basedneighbor information.
Tajima el al. stated a query framework for hypertext datain [4]. It is mainly about using a series of nodes corresponding to one topic as the data units in queries, rather than justan individual node. This query framework can help us when
our aim is to discover unknown data concerned with a topic
of our interest since it returns a collection of nodes talking
about the same topic. In their paper, a connected subgraphcorresponding to one topic is regarded as a cut. They concentrated in partitioning a graph into precise cuts by cutting all edges if there exists a topic change. An important
assumption is that similarity between two neihboring nodes(a) Iterative search process

must be higher when they are discussing the same topic thanwhen they are discussing diﬀerent topics. Therefore, similarity between the contents of two nodes is used to detect topic
changes. As our work also contains the same topic detection,in this degree, ours is similar to the above-mentioned work by
Tajima el al. But their approach can only be applied to tree
structures. As a result, if we want to apply this approach to
web pages, we have to transform the general graph generated
by links between web pages to a tree structure in advance.
Besides, their cut in web pages, does not extend over multiple web sites, which means this approach can only discover
web pages discussing the same topic within a single web site.
While on the other hand, our text landscape discovery has
no limitation in graph structure, and extends to web pages
or documents discussing similar, or semantic similar topics
from multiple web sites.

3. Text Landscape(b) Exploratory search process

図 2 Examples of search process of iterative search and ex-

The terminology “information landscape” has appearedsince 1990s. Russell [3] thought it is used to describe a way

The red triangle in (a) represents theof presenting diﬀerent views (personalised or community in-search target. Both iterative search and exploratory searchterest) of information resources to users, based on their in-are within the whole information space. Each result set ob-

terests and needs. An information landscape could be a set

tained by a query is denoted as a circle, whose size meansof web pages which link to certain resources. It also could

ploratory search.

the total number of returned documents. The nearby number means the search sequence.

be a view constructed dynamically according to user’s proﬁle. In [5], “information landscape” is deﬁned as the user’s
personal view of the information universe, which is intended

web spam in [9]. They were inspired by the hypothesis thatto represent a complete working environment. Information

linked hosts tend to belong to the same class: either bothresources are not separated out as standalone items, but in-are spam or both are non-spam. In their link-based feature

tegrated into people’s working and learning environments.

analysis, neighbor is referred to as neighboring documents inThat means the more important issue of “information land-a hyperlink environment. With the help of predicted labels

scape” is to dynamically lead users to new things, perhapsof neighboring hosts, spam classiﬁer can be retrained to gettheir potential interests.

“Text landscape” is subtly diﬀerent from the above-

4. 1 Text Similarity

mentioned “information landscape”. It is used to describe

This problem is as follows, given a web page as our input,

the whole knowledage environment about a query topic in aother web pages that are similar to the input are expected towell-organized way. It is supposed to support the ambigu-

be ﬂagged based on their similarity to the input one. Sup-ous exploratory search, especially those which has unknown

pose there is a web page dataset Pn as follows, Pn ={p1 , p2 ,goal before a query is issued, and user’s learning process.

..., pi , ..., pn }, where n is the total number of pages in thisDocuments that share the same, similar, or semantic similar

dataset. When any one page pi in the dataset is given as

topics with a document, are called “surrounding informa-

an input, the expected output is similarities between othertion” of the certain document. Here the topics contain not

pages in the dataset and the input page. The functional form

only those of a whole document, but also those extracted

representing this problem is as follows,from a part of a document, such as topic of a paragragh.
As a result, documents of a query, together with their “sur-Sim(pi , pj )afore-mentioned query. Since the whole knowledge environ-

where pj is any other page in the dataset, 1 <
= i <
= n,
<
1 <
j
n.
It
is
desiredthat
thisfunction
returnsareal= =ment about a query topic is well organized, it is no longer

number, which has a high value when the content of pj isa trial-and-error journey through the information space as

highly similar to that of pi , and vice versa.

rounding information”, build up the “text landscape” of thethe destination. Users can ﬁgure out their subconscious goal

4. 2 Text Adjacency

with the help of “text landscape”.

As we discussed in Section 3., “surrounding information”

For example, a problem we often encounter. Consider anof a document is those documents that share the same, sim-

academic paper discussing ranking algorithm in information

ilar, or semantic similar topics with the certain document.

retrieval architecture, such as [11]. When we read a paper, ofIn this paper, the degree that two documents or web pages

course not all methods or terminologies can we understand.

share the same, similar, or semantic similar topics is referred

At this time, one choice is to read some references of thisto as “text adjacency”. In other words, “text adjacency” ispaper. Suppose we have little knowledge about PageRanka kind of partial similarity between two documents or web

algorithm mentioned in [11]. Then we may check one of [11]’s

pages.

references “The Anatomy of a Large-Scale Hypertextual Web

The problem is, then similar to that of text similarity.

Search Engine” written by S. Brin and L. Page. Still, if weGiven a web page as our input, it is expected that otherencounter other unknown things, we will read references ofpages adjacent to this page will be highlighted in terms of

S. Brin and L. Page’s above-mentioned paper. In a simple

adjacency with the input one. When any one page pi in thecase, just treat references as surrounding information, thendataset is given as an input, the expected output is adjacen-

the relations among all related papers can show us a brief in-cies between other pages in the dataset and the input page.

troduction of a certain research ﬁeld. For another example,

The functional form representing this problem is as follows,

consider an event stated in a news web page. A simple caseis that the causation and the result of the event will be alsoAdj(pi , pj )presented to users as well. The “text landscape” of the event1<=i<= n, 1 <=j<= n. A high value will be returned when pj

is, but not limited to the detailed develpment, the causation

is highly adjacent to pi , and vice versa.

and the result of the event.

4. Basic Idea

5. Our approach
Here we explain our graph-based ranking method Panora-

The problem described in this paper is as follows:maRank for “text landscape” in detail. It is a revision of our•previous work [14]. Roughly speaking, we employ Panorama-

Input: A web page of search result of a query, indi-cated by user•

Output: The whole “text landscape” of the query

topic
As our notion is to present user “surrounding information”

Rank to discover a “text landscape” of a user-indicated web
page. Text similarity and text adjacency is combined in our
method to ﬁnd “surrounding information” of a certain web
page.

of an input query to completely show the knowledge environ-5. 1 Calculating Text Similarity

ment, the following two relations between documents, text

Text similarity is obtained by calculating similarity be-

similarity and text adjacency, are considered as key factors

tween two web pages or documents. Here we employ theto detect “surrounding information” of a speciﬁc query.

widely-used vector space model [1]. In this model, given a
set of n documents, each document di is represented by a

5. 3 PanoramaRank for “Text Landscape”

t-dimensional vector,

In [11], VisualRank, which is an inferred visual similarity

V (di ) = (wi1 , wi2 , ..., wit ),graph-based ranking model for image-ranking problems, waswij representing the weight of the jth term, t representing the

introduced. In this model, edge weights have also been con-number of unique terms that occurs in any of d1 , d2 , ..., dn .

sidered when estimating the score associated with a vertex

In the equation above,in the graph. The random walk algorithm is employed toWwij = tfi,j · log ,wrank images based on the visual hyperlinks among the images. It is assumed that if a user is viewing an image, otherwhere tfi,j is the term frequency of term j in document di ,log W
is the inverse document frequency. Here W is the totalw
number of documents in the document set, w is the numberof documents containing the term j.
Thus, Sim(pi , pj ) which denotes the content similarity between web page pi and pj , is computed by cosine similarityof their feature vectors, deﬁned as below,

related(similar) images may also arouse the user’s interest.
Similar to PageRank algorithm, if image u is visually hyperlinked to image v, such hyperlink is treated as a vote of
conﬁdence, which means it is possible that the user will gofrom viewing u to viewing v. As a result, images related(similar) to the query image will have many other images
pointing to them and will therefore be viewed often. In [11],V (pi ) · V (pj )Sim(pi , pj ) =|V (pi )||V (pj )|∑t
m=0 wm,i · wm,j
√∑
= √∑
.
t
t
2
2
w
·
w
m=0 m,im=0 m,jwhere p = [ n1 ]n×1 . S ∗ is the column normalized adjacency

5. 2 Calculating Text Adjacency

matrix S, where Su,v denotes the visual similarity between

In our previous work [14], adjacency between two images

image u and v. d is a damping factor between 0 and 1, which

given n images, VisualRank (VR) is iteratively deﬁned as
V R = dS ∗ × V R + (1 − d)pis deﬁned as the overlap degree between them. It can be also
considered as an approach to ﬁnd similarity of fragments ofis usually set to be greater than 0.8.
Here, let GS = (V, ES ) be an undirected graph with a set

the two images. Similarly, in text architecture, text adja-

of vertices V and a set of edges ES , where ES is a subset

cency is partial similarity between two web pages. Beforeof V × V , ES = {e = (u, v)|u is similar to v}. Likewise, let“text adjacency” is computed, each web page must be di-

GA = (V, EA ) be an undirected graph with a set of vertices
V and a set of edges EA , where EA is a subset of V × V ,vided into several parts.
One way to get “text adjacency” between web page pi and

EA = {e = (u, v)|u is adjacent to v}. Then the ﬁnal similar-

pj is to take the maximum similarity among those of all part-ity/adjacency graph(SA graph for short below) for ranking

pairs, shown as follows:is deﬁned as G = GS ∪ GA , where ES ∩ EA =| ϕ.

Adj(pi , pj ) =max

Sim(x, y)x∈P art(pi ),y∈P art(pj )In our case, we apply the above-mentioned random walkalgorithm to the deﬁned SA graph G. Thus, after iterative

where P art(pi ) and P art(pj ) are referred to as a part of web

calculation, web pages not only content similar but also dis-

page pi and pj , respectively.

cussing the same, or similar, or semantic similar topics, withIn order to avoid web pages with high text similairities

speciﬁed page(s), will come to the top. For instance, if agathering together, we also consider a way to give a penalty

web page about an event is assigned as the starting point, ain terms of “text similarity” between two web pages, whichsimple case is that the causation and the result of this event

is deﬁned as follows:maxx∈P art(pi ),y∈P art(pj ) Sim(x, y)Adj(pi , pj ) =Sim(pi , pj )will be also returned as well. The surrounding information

where P art(pi ) and P art(pj ) are referred to as a part of web
page pi and pj , respectively.
There are many algorithms with which a full-length document can be divided into several parts in terms of theirof the assigned page is, but not limited to the causation andthe result of this event.
Given n web pages, our proposed PanoramaRank (PR) is
deﬁned as follows:
P R = dS ∗ × P R + (1 − d)p

diﬀerent subtopics discussing. In [2], using orthographicallywhere pi is the initial value of Vi , and we refer to vertex Vi

marked segments supplied by the author to determine topic

of a page pi . d is a damping factor, and we set it to 0.85

boundaries is conﬁrmed consistent with human judgment. So

in our evaluation experiments empirically. S ∗ is the column

in this paper, we just break the document into paragraphs

normalized adjacency matrix S, but here Spi ,pj denotes theand regard similarity of paragraphs between two documents

combination of text similarity and text adjacency between

as “text adjacency” between these two documents.

web page pi and pj .

A simple way to set initial value of a page is whether thispage is indicated by user as an input or not, deﬁned as follows:pi ={6. Conclusions
In this paper, we proposed a method that can be used to1, web page pi is speciﬁed as an input

ﬁnd the whole “text landscape” of the query topic according

0, otherwise

to a user-indicated document, which helps searcheres easily

We also consider another way to set initial value of a page
based on semantic similarity among keywords of the page
and those of input page. For computing sementic similarity
between two terms, we employ normalized Google distanceintroduced by Cilibrasi and Vitanyi in [10]. It is derived fromthe number of hits returned by the Google search engine
for words and phrases from the world wide web. Words
or phrases with the same or similar meanings tend to be“close” when evaluating their semantic similarity by Google
distance, while words or phrases with dissimilar meanings
tend to be farther apart. Especially, the normalized Google
distance (NCG) between two words x and y is deﬁned as
below:N GD(x, y) =max{log f (x), log f (y)} − log f (x, y)
log N − min{log f (x), log f (y)}where f (x) denotes the number of pages containing word
x, and f (x, y) denotes the number of pages containing bothword x and word y, as searched by Google search engine. By
increasing N , the N GD is decreased, and everything tends
to get closer together, while by decreasing N , the NGD is increased, and everything tends to get further apart. In theirexperiments, the number of web pages indexed by Google atthe time of their writing is used as normalizing factor N .
Thus, initial value of web page pi in terms of semantic similarity among keywords of the page and those of input page
pQ is deﬁned as:ous and unknown goal. Unlike the state-of-the-art searchtechnolodies only focus on similarity of documents’ contents
to get extremely similar documents, we aimed to extend
search result to “surrounding information” of speciﬁed document(s). For the sake of seeking “surrounding information”,
the concept of “text adjacency” was introduced into searchframework. With the help of our proposed method, PanoramaRank for text landscape, documents not only content similar but also discussing the same, or similar, or semantic similar topics, with speciﬁed document(s), will come to the top.
It means ﬁnally we can retrieve more “related” information,
not only “relevant” ones.
Because of the lack of time, we did not do evaluations ofour method. So in the near future, the ﬁrst thing we needto do is to evaluate PanoramaRank for text landscape. We
plan to do our evaluations based on a news web page dataset.
Several text landscape about diﬀerent query topics are prepared in advance, while web pages in each text landscapeare viewed and judged by humans to make sure they are discussing the same, or similar, or semantic similar topics witha certain topic. Take last-minute retirement of civil servantsfor instance. Not only web pages about the current situationof the last-minute retirement, but also pages of the causation led to this incident, the result caused by this incident,
and attitudes of multitude as well. Given any page from thedataset, such as a page about the last-minute retirement of

pi = Θx∈Keyword(pi ),y∈Keyword(pQ ) N GD(x, y)
where Θ is considered as:
Θ = max, or avg, or

ﬁnd or clear their target, or even awake to their ambigu-

max − min.
max

Keyword(pi ) and Keyword(pQ ) are referred to as keyword

civil servants, each returned page will be judged in termsof whether it is a page belonging to the text landscape ofthe last-minute retirement of civil servants indeed. In otherwords, we endeavor to evaluate the precision of discoveringa text landscape by our proposed method.

set of web page pi and pQ , respectively. In the experiments

Also, the time factor is an important factor for “text land-

we plan to do in the near future, just take words, or phrases

scape” discovery, especially for newspaper articles. With thein each news title as keywords of that news web page, sincelapse of time, the progress of an event is changing beyond all

for newspaper article, news title is typically a good summary

doubt, sometimes even to the completely opposite direction.

of its content. Of course, other keyword extraction algo-

Therefore, our second step is to bring the time factor intorithms such as TextRank [8], can be applied here to extract“text landscape” discovery.

keywords of a web page, or document.

Acknowledgements

The weight between web page pi and pj in SA graph G
is deﬁned as the arithmetic mean of their text similarity
Sim(pi , pj ) and their text adjacency Adj(pi , pj ).
Spi ,pj = α · Sim(pi , pj ) + (1 − α) · Adj(pi , pj )where α is the weight factor, whose default setting is 0.5.

This work was supported in part by KAKENHI (Nos.
24240013, 24680008).

文

献[1] G. Salton, A. Wong, C. S. Yang, “A vector space model for
automatic indexing”, Communications of the ACM, v.18,
n.11, pp. 613–620, Nov. 1975
[2] Marti A. Hearst, Christian Plaunt, “Subtopic structuring
for full-length document access”, Proceedings of the 16th
annual international ACM SIGIR conference on Researchand development in information retrieval, pp. 59–68, June
27-July 01, 1993, Pittsburgh, Pennsylvania, United States[3] R. Russell, “Mining the information landscapes: the Agora
Project”, The New Review of Information and Library Research. 4, pp 121-127, 1998
[4] Keishi Tajima , Yoshiaki Mizuuchi , Masatsugu Kitagawa ,Katsumi Tanaka, “Cut as a querying unit for WWW, Netnews, e-mail”, Proceedings of the 9th ACM conference onHypertext and hypermedia : links, objects, time and space—structure in hypermedia systems: links, objects, time andspace—structure in hypermedia systems, p.235-244, June
20-24, 1998, Pittsburgh, Pennsylvania, United States[5] R. Heseltine, “Alice through the looking glass: information
spaces for a new learning generation”, Information Landscapes for a Learning Society: Networking and the futureof libraries 3 ed. S. Criddle, L. Dempsey and R. Heseltine.
London: Library Association publishing (in association with
the UK Oﬃce for Library and Information Networking), pp.
xv-xxiii, 1999
[6] Andrei Broder, “A taxonomy of web search”, ACM SIGIR
Forum, v. 36, n. 2, pp. 3–10, Fall 2002
[7] Daniel E. Rose, Danny Levinson, “Understanding user goals
in web search”, Proceedings of the 13th international conference on World Wide Web, pp. 13–19, 2004
[8] R. Mihalcea, P. Tarau, “Textrank: Bringing order intotext”, Proceedings of the conference on Empirical Methods
in Natural Language Processing, pp. 404–411, 2004
[9] Carlos Castillo, Debora Donato, Aristides Gionis, Vanessa
Murdock, “Know your neighbors: web spam detection using
the web topology”, Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 423–430, 2007[10] Rudi L. Cilibrasi, Paul M. B. Vitanyi, “The Google Similarity Distance”, IEEE Transactions on Knowledge and Data
Engineering, v.19 n.3, pp. 370–383, March 2007[11] Yushi Jing, Shumeet Baluja, “VisualRank: Applying
PageRank to Large-Scale Image Search”, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.30
n.11, pp. 1877–1890, November 2008[12] Xiaojun Wan, Jianguo Xiao, “Single document keyphrase
extraction using neighborhood knowledge”, Proceedings ofthe 23rd national conference on Artiﬁcial intelligence, v. 2,
pp. 855–860, 2008[13] Ryen W. White, Resa A. Roth, “Exploratory search : beyond the query-response paradigm”, 2009[14] Meng Zhao, Hiroaki Ohshima, Katsumi Tanaka, “Panoramic
image search by similarity and adjacency for similar landscape discovery”, Proceedings of the 13th International
Conference on Web Information Systems Engineering, pp.
284–297, 2012