Query refinement and user relevance feedback for contextualized image retrieval

Chandramouli, Krishna; Kliegr, Tomáš; Nemrava, Jan; Svátek, Vojtěch; Izquierdo, Ebroul

doi:10.1049/cp:20080356

Cited by 25 publications

(14 citation statements)

References 12 publications

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“…Since WordNet has narrow coverage for domain specific queries [20], we use association rules to exploit collection-dependent word relationships. We examine the vocabulary and add the words with both top 10 highest conf idence and support with the original query words into the query expansion.…”

Section: A Query Expansionmentioning

confidence: 99%

Faceted Subtopic Retrieval: Exploiting the Topic Hierarchy via a Multi-modal Framework

Sang¹,

Xu²

2012

JMM

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Abstract-The overwhelming amount of web videos posted on the social media websites make effective browsing and search a challenging task. The user-provided metadata, has been proved useful in large-scale video organization and retrieval. Search result clustering, which utilizes the associated metadata to cluster the returned results into semantic groups according to its involved subtopics, has shown its advantages. Most of the existing works on search result clustering are devoted to solving the ambiguous problem resulted from general queries. In this paper, we propose the problem of faceted subtopic retrieval, which focus on more complex queries concerning political and social events or issues. Hierarchical topic model (hLDA) is adapted to exploit the intrinsic topic hierarchy inside the retrieved collections. Furthermore, this paper offers a new perspective of multi-modal video analysis by exploring the pairwise visual cues deriving from duplicate detection for constrained topic modeling. We modify the standard hierarchical topic model by integrating: 1) query related Supervision knowledge (ShLDA) and 2) duplicate Relation constraints (RShLDA). Carefully designed experiments on web-scale video dataset validate the proposed method.

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Section: A Query Expansionmentioning

confidence: 99%

Faceted Subtopic Retrieval: Exploiting the Topic Hierarchy via a Multi-modal Framework

Sang¹,

Xu²

2012

JMM

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“…The value of Euclidean 2-norm distance between 1 u and the next input sample x , is compared withε . If it is greater, a new cluster which is centered at the location defined by x is created as 2 u , otherwise the elements of 1 u are updated as:…”

Section: Rbf Neural Networkmentioning

confidence: 99%

Relevance Feedback Using RBF for Content Based Image Retrieval

Zhou

Wang

et al. 2012

AEF

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Abstract. Conventional approaches to image retrieval are based on the assumption that relevant images are physically near the query image in some feature space. However, semantically related images are often scattered across several visual clusters. This leads to adapting multiple queries to represent a query in the feature space. Therefore, it is necessary to handle disjunctive queries in the feature space. In this paper, a new content-based image retrieval method with relevance feedback technique using RBF neural network learning is proposed. The method transfers the process of relevance feedback into a learning problem of RBF neural network. RBFNN can describe the distribution of positive feedback sample images in feature space with a set of neighboring clusters produced through constructing neural network, for accurately reflecting their semantic relevance. The performance of the method using RBFNN is evaluated on a database of 10,000 images. Experimental results demonstrate the effectiveness of the proposed method.

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“…THD also has the advantage of requiring no training and can use up-to-date on-line resources to find hypernyms in real time. The THD algorithm proposed here is an updated and expanded version of the algorithm used in our earlier work [8]. The outline of the steps taken to find a hypernym for a given entity in our THD implementation is as follows: Performing all these steps requires to carry out multiple information retrieval and text processing tasks.…”

Section: Targeted Hypernym Discoverymentioning

confidence: 99%

Combining image captions and visual analysis for image concept classification

Kliegr

Chandramouli

Nemrava

et al. 2008

Proceedings of the 9th International Workshop on Multimedia Data Mining: Held in Conjunction With the ACM SIGKDD 2008

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We present a framework for efficiently exploiting free-text annotations as a complementary resource to image classification. A novel approach called Semantic Concept Mapping (SCM) is used to classify entities occurring in the text to a custom-defined set of concepts. SCM performs unsupervised classification by exploiting the relations between common entities codified in the Wordnet thesaurus. SCM exploits Targeted Hypernym Discovery (THD) to map unknown entities extracted from the text to concepts in Wordnet. We show how the result of SCM/THD can be fused with the outcome of Knowledge Assisted Image Analysis (KAA), a classification algorithm that extracts and labels multiple segments from an image. In the experimental evaluation, THD achieved an accuracy of 75%, and SCM an accuracy of 52%. In one of the first experiments with fusing the results of a free-text and image-content classifier, SCM/THD + KAA achieved a relative improvement of 49% and 31% over the text-only and image-content-only baselines.

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Query refinement and user relevance feedback for contextualized image retrieval

Cited by 25 publications

References 12 publications

Faceted Subtopic Retrieval: Exploiting the Topic Hierarchy via a Multi-modal Framework

Faceted Subtopic Retrieval: Exploiting the Topic Hierarchy via a Multi-modal Framework

Relevance Feedback Using RBF for Content Based Image Retrieval

Combining image captions and visual analysis for image concept classification

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