Paciente B

Ioannis Anagnostopoulos, Christos Anagnostopoulos, George Kouzas, and Vergados Dimitrios School of Electrical and Computer Engineering, Heroon Polytechneiou 9, Zographou, 15773, Athens, Greece

[email protected]

Abstract. Nowadays most web pages contain both text and images. Neverthe- less, search engines index documents based on their disseminated content or their meta-tags only. Although many search engines offer image search, this service is based over textual information filtering and retrieval. Thus, in order to facilitate effective search for images on the web, text analysis and image proc- essing must work in complement. This paper presents an enhanced information fusion version of the meta-search engine proposed in [1], which utilizes up to 9 known search engines simultaneously for content information retrieval while 3 of them can be used for image processing in parallel. In particular this proposed meta-search engine is combined with fuzzy logic rules and a neural network in order to provide an additional search service for human photos in the web.

1

Introduction

Since the web is growing exponentially search engines cannot spider all the new pages at the same time due to the fact that they use different algorithms in order to index their ‘attached’ web pages. As a result they have different response time in updating their directories and the user may lose some useful information resources in case that he use the returned results from only one search service [1]. To overcome this prob- lem most users try to expand their results with the help of meta-search engines. Using such search-tools, additional information is provided, without having to know the query language for all search services, which, some of them offer the ability for im- age/picture search. However, in this kind of retrieval a large amount of inappropriate and useless information is often returned to the user. Therefore, the same problem occurs when using a meta-search engine since the returned merged results depend on the respective result of each used search service. Especially in case of inquiring hu- man photos the ratio of accurate information is very low, due to the fact that face images are highly variable and difficult to be interpreted. Hence, in order to minimize the information noise this paper suggests a meta-search engine, which combines fuzzy logic rules for human skin recognition joint with a probabilistic neural network for face detection. The innovation in using the proposed machine stands in the fact that the user after a multiple image/picture query can work off-line and bound his search for retrieving human photos.

G.A. Vouros and T. Panayiotopoulos (Eds.): SETN 2004, LNAI 3025, pp. 43–53, 2004. © Springer-Verlag Berlin Heidelberg 2004

44 Ioannis Anagnostopoulos et al.

2

Inquiring Photos in the Web

This section presents an example of using the proposed meta-search engine in order to reveal all possible results for an image query. It must be noted that the web search services used are depicted in Table 1, where AlltheWeb, AltaVista and Excite sup- port image search.

Figure 1 presents the GUI of the proposed engine in case of image query submis- sion. As it is shown AltaVista, Excite and AlltheWeb are engaged in the search while the rest are inactive, since they do not support queries for images. The meta-search interface also supports Boolean queries for both textual and image search. However, even if the search engines used support the Boolean retrieval model, their query syntax differs. In addition, the query-translation problem also presents a barrier from the different stemming algorithms or the stop-word lists that are involved in the query model of each search engine. As a result of all this inconsistency, the proposed meta- search engine translates the user query before submitting it in parallel to the selected search engines. A unified syntax is proposed and it is presented in Table 2. This syntax allows the user to submit more complicated queries such as “term1*#Exact Phrase# - term2”. In this case the proposed meta-search engine asks to get results from the se- lected search engines, having both term1 and an exact matching string, excluding term2. As presented in Figure 1, the user wants to collect all possible images concern- ing the query “Georgatos AND Inter”, seeking photos of a football player of Italian team Inter. The system simultaneously translates the query and submits it to the three selected search services, respectively. Table 3 holds all the returned results in terms of returned images that contain the player and other images that are irrelevant to photos of the football player. After merging the results and removing the duplicate fields the meta-search engine returned 27 images, from which only 14 of them are actually pho- tos that fulfill the submitted query.

Precise Photo Retrieval on the Web 45

Fig. 1. Image query interface

As it is obvious a significant amount of information is actually not relevant accord- ing to the respective query. The innovation in this paper stands in the fact that the user can further investigate among all the returned results in order to restore photos that include human faces. The existence of a human face in the proposed tool is crucial since it is implies a human presence in the image. The “PhotoSearch” button initiates a two-step off-line mechanism, which it briefly explained in the followings.