Relevance feedback methods for logo and trademark image retrieval on the web

Petrakis, Euripides G. M.; Kontis, Klaydios; Voutsakis, Epimenidis; Milios, Evangelos

doi:10.1145/1141277.1141532

Cited by 9 publications

(6 citation statements)

References 16 publications

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“…The evaluation of relevance feedback methods [14] demonstrated that term re-weighting based on text and image content is the most effective approach. The results demonstrate that term re-weighting is the most effective relevance feedback approach for all query types.…”

Section: Discussionmentioning

confidence: 99%

“…Relevance feedback [28,30,14] is the state-of-the-art approach for adjusting query results to the needs of the users. A common assumption is that there exists an ideal query (or matching method) that captures the information needs of the users.…”

Section: Relevance Feedbackmentioning

confidence: 99%

“…In the following, the existing framework of image retrieval with relevance feedback on the Web is extended to handle more sophisticated queries (e.g., queries by image example), by incorporating text and image content into the image retrieval and relevance feedback processes [14]. To do so, the concepts of text and image similarity of Sec.…”

Section: Relevance Feedbackmentioning

confidence: 99%

“…The method is extended to accommodate for the definition of image similarity by text and image content as follows [14]. Let R be the set of the N R most similar images (e.g., N R = 30).…”

Section: Term Re-weightingmentioning

confidence: 99%

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Intelligent Search for Image Information on the Web through Text and Link Structure Analysis

Petrakis

2008

Multimodal Processing and Interaction

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Searching for effective methods to retrieve information from the World Wide Web (WWW) has been in the center of many research efforts during the last few years. The relevant technology evolved rapidly thanks to advances in Web systems technology [1] and information retrieval research [15]. Image retrieval on the Web, in particular, is a very important problem in itself [8].The relevant technology has also evolved significantly propelled by advances in image database research [20].Several approaches to the problem of content-based image retrieval on the Web have been proposed and some have been implemented on research prototypes (e.g., ImageRover [23],WebSEEK [21]) and commercial systems. The last category of systems, includes general purpose image search engines (e.g., Google Image Search 1 , Yahoo 2 , Altavista 3 ) as well as systems providing specific services to users such as detection of unauthorized use of images, Web and e-mail content filters, image authentication, licensing and advertising.Image retrieval on the Web requires that content descriptions be extracted from Web pages and used to determine which Web pages contain images that satisfy the query selection criteria. The methods and systems referred to above differ in the type of content descriptions used and in the search methods applied. There are four main approaches to Web image search and retrieval.Retrieval by text content: Typically images on the Web are described by text or attributes associated with images in html tags (e.g., filename, caption, alternate text etc.). These are automatically extracted from the Web pages and are used in retrievals. Google, Yahoo, and AltaVista are example systems of this category. The importance of the various text fields in retrieving images by text content depends also on their relative location with regard to the location of the images within the Web pages [19].

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Section: Discussionmentioning

confidence: 99%

Section: Relevance Feedbackmentioning

confidence: 99%

Section: Relevance Feedbackmentioning

confidence: 99%

“…The method is extended to accommodate for the definition of image similarity by text and image content as follows [14]. Let R be the set of the N R most similar images (e.g., N R = 30).…”

Section: Term Re-weightingmentioning

confidence: 99%

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Intelligent Search for Image Information on the Web through Text and Link Structure Analysis

Petrakis

2008

Multimodal Processing and Interaction

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“…While methods for fusing together plain metadata and low-level features mainly in web-based search systems have been employed (e.g., [16], [3]), to the best of the authors' knowledge this is the first attempt to combine semantic and visual-based feature sources in order to provide a more efficient retrieval strategy.…”

Section: Combining Visual and Semantic Informationmentioning

confidence: 99%

A hybrid ontology and visual-based retrieval model for cultural heritage multimedia collections

Vrochidis

Doulaverakis

Gounaris

et al. 2008

IJMSO

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Abstract. Nowadays, an increasingly growing demand for advanced multimedia search engines is arising, as huge amounts of digital visual content are becoming available. The contribution of this paper is the introduction of a hybrid multimedia retrieval model accompanied by the presentation of a search engine that is capable of retrieving visual content from cultural heritage multimedia libraries as in three modes: (i) based on their semantic annotation with the help of an ontology; (ii) based on the visual features with a view to finding similar content; and (iii) based on the combination of these two strategies in order to produce recommendations. To achieve this, the retrieval model is composed of two different parts, a low-level visual feature analysis and retrieval and a high-level ontology infrastructure. The main novelty is the way in which these two co-operate transparently during the evaluation of a single query in a hybrid fashion, making recommendations to the user and retrieving content that is both visually and semantically similar. A search engine has been developed implementing this model which is capable of searching through digital libraries of cultural heritage collections, and indicative examples are discussed, along with insights into its performance.

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