Exploring Automatic Query Refinement for Text-Based Video Retrieval

Volkmer, Timo; Natsev, Apostol

doi:10.1109/icme.2006.262951

Cited by 16 publications

(10 citation statements)

References 5 publications

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“…retrieval component (based on speech transcripts). Our speech-based retrieval baseline is based on the JuruXML semantic search engine using story boundary and topic refinement approaches [8]. Visual and speech-based retrieval components are fused using topic dependent weights [9], where we learn the fusion weights on one corpus and apply the weights to the other.…”

Section: Methodsmentioning

confidence: 99%

Data Modeling Strategies for Imbalanced Learning in Visual Search

Tešić

Natsev

Xie

et al. 2007

Multimedia and Expo, 2007 IEEE International Conference On

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In this paper we examine a novel approach to the difficult problem of querying video databases using visual topics with few examples. Typically with visual topics, the examples are not sufficiently diverse to create a robust model of the user's need. As a result, direct modeling using the provided topic examples as training data is inadequate. Otherwise, systems resort to multiple content-based searches using each example in turn, which typically provides poor results. We propose a new technique of leveraging unlabeled data to expand the diversity of the topic examples as well as provide a robust set of negative examples that allow direct modeling. The approach intelligently models a pseudo-negative space using unbiased and biased methods for data sampling and data selection. We apply the proposed method in a fusion framework to improve discriminative support vector machine modeling, and improve the overall system performance. The result is an enhanced performance over any of the baseline models, as well as improved robustness with respect to training examples, visual features, and visual support of video topics in TRECVID. The proposed method outperforms a baseline retrieval approach by more than 18% on the TRECVID 2006 video collection and query topics.

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Section: Methodsmentioning

confidence: 99%

Data Modeling Strategies for Imbalanced Learning in Visual Search

Tešić

Natsev

Xie

et al. 2007

Multimedia and Expo, 2007 IEEE International Conference On

Self Cite

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“…It brings an additional 0.4 MAP improvement over the direct keyword matching approach. A more recent study (Volkmer and Natsev 2006) compared three automatic query expansion techniques including Rocchio-based query expansion, lexical-context based expansion and semantic annotation-based expansion on the TRECVID datasets. Surprisingly, their experiments have underscored the difficulty of automatic query expansion in video collections, because only one out of three approaches can gain higher average precision than the non-expansion baseline.…”

Section: Query Expansionmentioning

confidence: 99%

A review of text and image retrieval approaches for broadcast news video

Yan

Hauptmann

2007

Inf Retrieval

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The effectiveness of a video retrieval system largely depends on the choice of underlying text and image retrieval components. The unique properties of video collections (e.g., multiple sources, noisy features and temporal relations) suggest we examine the performance of these retrieval methods in such a multimodal environment, and identify the relative importance of the underlying retrieval components. In this paper, we review a variety of text/image retrieval approaches as well as their individual components in the context of broadcast news video. Numerous components of text/image retrieval have been discussed in detail, including retrieval models, text sources, temporal expansion methods, query expansion methods, image features, and similarity measures. For each component, we conduct a series of retrieval experiments on TRECVID video collections to identify their advantages and disadvantages. To provide a more complete coverage of video retrieval, we briefly discuss an emerging approach called concept-based video retrieval, and review strategies for combining multiple retrieval outputs.

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“…These approaches have shown promising retrieval results [96,64] by leveraging extra concepts. However, these approaches are also likely to bring in noisy concepts, and thus lead to unexpected deterioration of search results.…”

Section: Use Of Semantic Concepts For Automatic Retrievalmentioning

confidence: 99%

Extracting Semantics from Multimedia Content: Challenges and Solutions

Xie

Yan²

2008

Multimedia Content Analysis

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Multimedia content accounts for over 60% of traffic in the current internet [74]. With many users willing to spend their leisure time watching videos on YouTube or browsing photos through Flickr, sifting through large multimedia collections for useful information, especially those outside of the open web, is still an open problem. The lack of effective indexes to describe the content of multimedia data is a main hurdle to multimedia search, and extracting semantics from multimedia content is the bottleneck for multimedia indexing. In this chapter, we present a review on extracting semantics from a large amount of multimedia data as a statistical learning problem. Our goal is to present the current challenges and solutions from a few different perspectives and cover a sample of related work. We start with an system overview with the five major components that extracts and uses semantic metadata: data annotation, multimedia ontology, feature representation, model learning and retrieval systems. We then present challenges for each of the five components along with their existing solutions: designing multimedia lexicons and using them for concept detection, handling multiple media sources and resolving correspondence across modalities, learning structured (generative) models to account for natural data dependency or model hidden topics, handling rare classes, leveraging unlabeled data, scaling to large amounts of training data, and finally leveraging media semantics in retrieval systems.

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Exploring Automatic Query Refinement for Text-Based Video Retrieval

Cited by 16 publications

References 5 publications

Data Modeling Strategies for Imbalanced Learning in Visual Search

Data Modeling Strategies for Imbalanced Learning in Visual Search

A review of text and image retrieval approaches for broadcast news video

Extracting Semantics from Multimedia Content: Challenges and Solutions

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