&lt;title&gt;MetaSEEk: a content-based metasearch engine for images&lt;/title&gt;

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

doi:10.1117/12.298436

Cited by 42 publications

(19 citation statements)

References 9 publications

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“…Gaughan et al [8] ranks the video shots based on the summation of feature scores and automatic speech retrieval scores, where the influence of speech retrieval is at 4 times that of any other features. Benitez et al [2] proposed content-based meta-search image search engine, called Metaseek. It assigns the new query images to one of the predefined clusters and selects one of the target image search engines based on their previous success of handling the similar queries.…”

Section: Related Workmentioning

confidence: 99%

Learning query-class dependent weights in automatic video retrieval

Yan

Yang

Hauptmann

2004

Proceedings of the 12th Annual ACM International Conference on Multimedia

108

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Combining retrieval results from multiple modalities plays a crucial role for video retrieval systems, especially for automatic video retrieval systems without any user feedback and query expansion. However, most of current systems only utilize query independent combination or rely on explicit user weighting. In this work, we propose using query-class dependent weights within a hierarchial mixture-of-expert framework to combine multiple retrieval results. We first classify each user query into one of the four predefined categories and then aggregate the retrieval results with query-class associated weights, which can be learned from the development data efficiently and generalized to the unseen queries easily. Our experimental results demonstrate that the performance with query-class dependent weights can considerably surpass that with the query independent weights.

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Section: Related Workmentioning

confidence: 99%

Learning query-class dependent weights in automatic video retrieval

Yan

Yang

Hauptmann

2004

Proceedings of the 12th Annual ACM International Conference on Multimedia

108

View full text Add to dashboard Cite

show abstract

“…The algorithm states that the topicality of a page increases with the number of hyperlinks to it from other topical pages. Beigi et al [9] introduced MetaSEEkA, a meta-search engine which is based on content, used for discovering images on the Web based on their visual information. MetaSEEkA was designed to penetratingly select and interface with diverse on-line image search engines by ordering their performance of user queries for different classes.…”

Section: Related Workmentioning

confidence: 99%

A Score based Web Page Ranking Algorithm

ShamiulAmin¹,

Kabir²,

Kabir³

2015

IJCA

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With the explosive growth of information in the Web, users face difficulties while finding their desired information. Search engine helps the user by retrieving useful information from this huge collection based on his/her search query and presents a list of relevant web pages as a search result. However, without proper ranking of pages in the result through the relevancy of pages to the search query, the user may need to explore the whole list for discovering the appropriate page(s), thereby involving huge search time. Although a number of ranking algorithms such as HITS, PageRank, Weighted PageRank and etc., are developed to assist the search engine, but none of them provides page ranking with high accuracy. In this paper, we propose a scorebased web page ranking algorithm involving web content mining and usage information of the pages. Our algorithm considers both syntactical and semantic matches of the search query to the pages. For a web page, syntactical score is calculated based on the total number of exact matches of the search words in the page. Besides, semantic score is measured using synonym matches of the search words. Moreover, we incorporate the usage information of the pages as page popularity in order to comprise the user interest in the ranking order. The total relevant score of each page is calculated using the summation of the syntactical and semantic scores of the page and its page popularity. Finally, the pages are ranked according to their total relevant score. Based on several performance evaluation measures, experimental results show considerable improvement in the page ranking using our algorithm as compared to other known approaches. General TermsSearch Engine, Ranking Algorithm, Web content mining, Web usage mining.

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“…Multimedia Search Components and Meta-Search: Many of the earliest image and video retrieval systems recognized the diversity of possible information sources available in visual databases and incorporated a variety of tools for enabling search over visual content [12], [18], [19], [35], [36]. One such tool is the query-by-example search method, whereby users could provide external examples, or use images from within the database to find other images that were similar in various low-level features, such as color distribution or texture.…”

Section: B Multimodal Search Over Image and Video Collectionsmentioning

confidence: 99%

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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<title>MetaSEEk: a content-based metasearch engine for images</title>

Cited by 42 publications

References 9 publications

Learning query-class dependent weights in automatic video retrieval

Learning query-class dependent weights in automatic video retrieval

A Score based Web Page Ranking Algorithm

Query-Adaptive Fusion for Multimodal Search

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