The Photo News Flusher:
A Photo-News Clustering Browser
Tatsuya Iyota and Keiji Yanai
Department of Computer Science,
The University of Electro-Communications
1–5–1 Chofugaoka, Chofu-shi, Tokyo, 182–8585 Japan{iyota-t,yanai}@mm.cs.uec.ac.jp
Abstract. We propose a novel news browsing system that can clusterphoto news articles based on both textual features of articles and image
features of news photos for a personal news database which is built byaccumulating Web photo news articles. The system provides two typesof clustering methods: normal clustering and thread-style clustering. Itenables us to browse news articles over several weeks or months visually
and ﬁnd out useful news easily. In this paper, we describe an overview
of our system, some examples of uses and user studies.

1Introduction

Many commercial news sites exist on the Web, and they deliver a lot of new
articles to us every day. Since data on the Web can be collected automaticallyby crawler programs, we can accumulate news articles on the Web automaticallyand build a personal news database with almost no cost. We can gather morethan one thousand articles a month, so that it is very diﬃcult to watch all ofthem and ﬁnd out interesting articles out of such huge news database.
In this paper, we propose a novel news browsing system which can clusterphoto news articles based on both textual features of articles and image features of news photos for a personal news database on PC. The system provides
two types of clustering methods: normal clustering and thread-style clustering.
For the normal clustering, we can adjust weights of textual features and image features, and for the thread-style clustering, we can control width of thread
branches with a novel method. Ide et al. [1] extracted topic threads from a largescale TV news video corpus and created a thread-based interface which enables
users to browse news articles related to the same topic along time series. This
topic threading is helpful to browse a large amount of news articles, so that
we import and modify it for our system as one of the clustering methods. By
these functions, the proposed system enables us to browse news articles over
several weeks or months visually and ﬁnd out useful news easily. In this paper,
we describe an overview of our system, examples of uses, and user studies.

2Proposed System

Regarding Web news search, it is general to search for news articles without
photos using only textual information. On the other hand, our targets are news
articles with photos, which have a special characteristic that it can be understood intuitively with just a look without reading. Our purposed system cluster
news articles using both textual information from text news articles and visual
information from photo articles. Since photos have the advantage of being visually recognizable, results of clustering are also easy to understand visually withjust a look. We aim to achieve a system which enables us to ﬁnd interesting news
articles easily taking advantage of photos as visual clues.

IIFig. 1. Structure of the proposed system which consists of the four parts.

We collect news articles with photos from the Yahoo Photo News Japan.
Being diﬀerent from usual news articles, the main contents of articles of theYahoo Photo News are photos. In addition to a photo, each article has a title
and a short main text which explain the contents of the photo. Note that all
articles are written in Japanese, since the Photo News site we use in our workis the Yahoo Photo News Japan. But our method did not rely on a speciﬁclanguage, so we can adjust our system for the other languages easily.
Our system consists of four parts: News Collection Part, Feature Extraction Part, User Interface Part, and Clustering Part (Fig.1). The News
Collection Part and the Feature Extraction Part are carried out once a day as oﬀline preprocessing. The User Interface Part and the Clustering Part are carriedout on-line.
News Collection In the News Collection Part, we gather titles, main texts,and photos of news articles by following the links from the top page of Yahoo
Photo News. If the Web page of the photo news includes a link to a usual news
article which has no photos, we collect the main text of the normal news article
as a “linked text” of the photo news article.
Next, we classify news articles collected into seven categories (domestic, international, business, entertainment, sports, computer, and local). The sevencategories are already classiﬁed by the Yahoo Photo News and can be distinguished from the URL of news articles. In addition, we prepare “all” to which
all articles belongs.
Feature Extraction In the Extraction Part, we extract image features fromthe gathered photos and textual features from the titles, the main texts and thelinked texts gathered in the Collection Part.
As image feature, we use color histograms computed in the Lu∗ v ∗ color space
as color image features. Each histogram quantizes the color space into 64 (4 foreach axis) bins. In addition, we also use the bag-of-keypoints histogram [2] which
consists of 300 bins as texture image features.
As textual feature, we use the vector space model for textual features. Eachelement of keyword vectors is weighted by the entropy-based TF-IDF. Similarity
between articles is calculated as follows:
sim = weight ∗ (simimg color + simimg texture ) + (1 − weight) ∗ simtext (1)where simimg color is the similarity calculated based on color image features,
simimg texture is the similarity calculated based on bag-of-keypoint-based texture image features, simtext is the similarity calculated based on textual features,and weight (0 ≤ weight ≤ 1) is a weight of image features. W eight is adjusted
by a slider on the User Interface. Similarities based on both color and texture
image features are calculated by the histogram intersection. Before calculating

III

similarities, each feature vector is normalized so that the sum of its elements is
1. Similarity based on textual features is calculated by the cosine similarity.
User Interface (UI) A user selects a term and a category of news articles
and set weight based on his/her preference on the Web-based UI. W eight is theratio of the textual feature and the image feature used in similarity calculation
for clustering. The Web-based user interface provides us with a slider to set aweight of textual features and image features.
Clustering In the Clustering Part, the system clusters news articles according
to similarity of feature vectors extracted in the Feature Extraction Part, andshows the article which is closest to the mean of the cluster as the representative
article of each cluster. We can see all the images of the cluster by selecting“display all images” on the UI.
The proposed system provides two kinds of clustering: k-means-based normal
clustering and time-series-based thread clustering.
Thread clustering groups articles related to the same topic and show them inthe time order. Here, “thread” means series of articles which are related to thesame one topic in the time order. If several articles are not similar each other but
all of them are similar to a certain article, “branching” will be made. “Thread”
can includes branches, and is composed as tree-structure in general.
By thread-clustering, we extract a thread which starts from a certain topic
a user selects from the result of normal k-means clustering, and show several
articles arranged as a short comic strip. They enable us to ﬁnd out the ﬂow orrelation of aﬀairs or events more easily. In the UI, a user also select a constant
α which decides what extent of topic drift in a thread is allowed. “Topic drift”
means that the main topic of a thread is changing gradually along time progress.
This “topic drift” control by a constant is one of novelties of our system, compared to Ide et al. [1].
To ﬁnd child articles which are highly related to the parent and their timestamps are always newer than the parent’s. “Highly related” is deﬁned by thefollowing condition:sim(vn , Vn−1 ) ≤ T(2), where{vn(n = 0)
Vn =(3)vn + αVn−1 (n ≥ 1)
α : a constant which controls “topic drift” (0 ≤ α ≤ 1)
 T : a threshold v : a feature vector of n-th article
nVn : an n-th accumulative mean vector

3

Example Uses and a User Study

Use of Normal Clustering We assume a user have an interest on the Japannational team of World Cup of soccer. At ﬁrst, the user selects “two months”
as the term of news articles and “sports” as the category. Next, the user selects“text 100%” as the weight of textual and image features so that the clusters
are associated with a sub-category of sports such as soccer and golf (Fig.2). The
representative image in the third cluster is soccer, so we can estimate the third
cluster contains many soccer articles.
In the next step, the user carries out re-clustering for the third cluster withtext 50% and image 50% in order to divide soccer articles into some clusters. Asa result, the user ﬁnd out a ”Japan Soccer W-cup” cluster which includes a lotof articles related to the Japan National Soccer Team (Fig.3).

IV

Use of Thread Clustering We assume that the thread clustering is used forsearching for related articles along time series after a user ﬁnd out an interestingarticle by the normal clustering.
Fig.4 shows the thread on the food poisoning aﬀair in the Japanese Domestic
news which was one of biggest news in early 2007 in case that α is 0, while Fig.5
shows the thread on the same topic in case that α is 0.9. These results indicatesthe extent of branching is adjusted by varying the value of α.

Fig. 2. Clustering result of sport news articles.

Fig. 5.
ThreadFig. 3. ”Japan Soccer W-cup” cluster． Fig. 4. Thread (α = 0). (α = 0.9).

User Study We made the brief user study on the proposed system. We asked
seven subjectives to compare the proposed system with a baseline system regarding how easily to search for interesting articles based on their own preferences.
The interface of the baseline system is similar to the Yahoo Photo News site.
The subjectives evaluated both the baseline and the proposed system with ascore from 1 to 5. As a result, the proposed system and the baseline system
obtained 3.86 and 2.43 on average, respectively. By applying Student’s T-test,
the diﬀerence on the average scores was proved to be signiﬁcant.

4Conclusions

We proposed a new photo news clustering browser which provided normal andthread clustering. Its eﬀectiveness was proved by the user study.

References
1. Ide, I., Mo, H., Katayama, N.: Threading news video topics. In: Proc. of ACM
SIGMM MIR (2003) 239–246.
2. Csurka, G., Bray, C., Dance, C.R., Fan, L.: Visual Categorization with Bags ofKeypoints. In: Proc. of ECCV WS on Stat. Learn. in CV, (2004) 1–22.