Using relevance feedback in content-based image metasearch

Benitez, Ana B.; Beigi, Mandis; Chang, Shih‐Fu

doi:10.1109/4236.707692

Cited by 49 publications

(21 citation statements)

References 8 publications

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“…In the MetaSeek system [13], we saw the use of relevance feedback (where users mark the top returned results as relevant or not) as a method for gaining approximate relevance labels for past queries. While relevance feedback has been an often-investigated technique in information retrieval, it has not been shown to be popular among users in any widespread application.…”

Section: Learning Relevancementioning

confidence: 99%

“…Benitez et al [13] use a similar approach for queryby-image-example queries: the classes of queries are represented as centroids in image feature space, and incoming query images are matched to a previous set of queries based on the distance in feature space. Kang and Kim [5], on the other hand, build language models for the various types of documents that are relevant to the classes of queries in their system and classify incoming queries based on their likelihood of being generated by each of the language models.…”

Section: A Understanding Search Intentmentioning

confidence: 99%

“…Benitez et al [13] proposed a meta-search application for image and video retrieval, which disseminated incoming queries to many of the openly available visual search engines of the time and combined the results to be returned to the user. Fig.…”

Section: B Multimodal Search Over Image and Video Collectionsmentioning

confidence: 99%

“…Many research efforts have proposed various methods for adapting retrieval strategies according to the query provided by the user in a number of search applications. One such strategy is to predefine a set of query Bclasses,[ which are sets of queries in which the optimal search strategies are similar for all queries contained within the class [3]- [5], [7], [11], [13], [14], [17]. Incoming queries can be classified into one of the classes based on some light natural language analysis of the query.…”

Section: Introductionmentioning

confidence: 99%

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Query-Adaptive Fusion for Multimodal Search

2008

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| We conduct a broad survey of query-adaptive search strategies in a variety of application domains, where the internal retrieval mechanisms used for search are adapted in response to the anticipated needs for each individual query experienced by the system. While these query-adaptive approaches can range from meta-search over text collections to multimodal search over video databases, we propose that all such systems can be framed and discussed in the context of a single, unified framework. In our paper, we keep an eye towards the domain of video search, where search cues are available from a rich set of modalities, including textual speech transcripts, low-level visual features, and high-level semantic concept detectors. The relative efficacy of each of the modalities is highly variant between many types of queries. We observe that the state of the art in query-adaptive retrieval frameworks for video collections is highly dependent upon the definition of classes of queries, which are groups of queries that share similar optimal search strategies, while many applications in text and web retrieval have included many advanced strategies, such as direct prediction of search method performance and inclusion of contextual cues from the searcher. We conclude that such advanced strategies previously developed for text retrieval have a broad range of possible applications in future research in multimodal video search.

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Section: Learning Relevancementioning

confidence: 99%

Section: A Understanding Search Intentmentioning

confidence: 99%

Section: B Multimodal Search Over Image and Video Collectionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Query-Adaptive Fusion for Multimodal Search

2008

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“…In [14], an image recommendation system based on the influence of affective metadata (metadata that describe the user's emotions) is presented. Low level properties have also been used for recommendation, taking advantage of CBIR methods combined with relevance feedback techniques (see [15]). …”

Section: Image Recommendation Typesmentioning

confidence: 99%

Social and Content Hybrid Image Recommender System for Mobile Social Networks

et al. 2012

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One of the advantages of social networks is the possibility to socialize and personalize the content created or shared by the users. In mobile social networks, where the devices have limited capabilities in terms of screen size and computing power, Multimedia Recommender Systems help to present the most relevant content to the users, depending on their tastes, relationships and profile. Previous recommender systems are not able to cope with the uncertainty of automated tagging and are knowledge domain dependant. In addition, the instantiation of a recommender in this domain should cope with problems arising from the collaborative filtering inherent nature (cold start, banana problem, large number of users to run, etc.). The solution presented in this paper addresses the abovementioned problems by proposing a hybrid image recommender system, which combines collaborative filtering (social techniques) with content-based techniques, leaving the user the liberty to give these processes a personal weight. It takes into account aesthetics and the formal characteristics of the images to overcome the problems of current techniques, improving the performance of existing systems to create a mobile social networks recommender with a high degree of adaptation to any kind of user.

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Visual Database

Yang¹,

Kunii²

2008

Wiley Encyclopedia of Computer Science and Engineering

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This article introduces principles and techniques developed for the management of visual data and for supporting visual queries against databases that manage the visual data. It presents an overview of well‐established approaches that are used in visual databases today for image feature extraction, visual data retrieval, indexing, and user query interfaces. Example visual database systems are described briefly. Finally, this article gives a list of survey papers that address technical issues related to visual data management.

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Using relevance feedback in content-based image metasearch

Abstract: MetaSeek is an image metasearch engine developed to explore the query of large, distributed, online visual information systems. The current implementation integrates user feedback into a performance-ranking mechanism.

Cited by 49 publications

References 8 publications

Query-Adaptive Fusion for Multimodal Search

Query-Adaptive Fusion for Multimodal Search

Social and Content Hybrid Image Recommender System for Mobile Social Networks

Visual Database

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