Probability-based fusion of information retrieval result sets

Lillis, David; Toolan, Fergus; Mur, Angel; Peng, Ling; Collier, Rem W.; Dunnion, John

doi:10.1007/s10462-007-9021-x

Cited by 9 publications

(8 citation statements)

References 28 publications

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“…Experimental results show better effectiveness when using this approach than traditional retrieval (i.e., using cosine distance). Eventually, the Probfuse algorithm proposed in [18], whose aim is to combine results from several IR algorithms, outperforms the widely used CombMNZ algorithm.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Reuse of Past Search Results

Gutiérrez-Soto

Hubert

2014

Lecture Notes in Computer Science

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract. In this paper, a new Monte Carlo algorithm to improve precision of information retrieval by using past search results is presented. Experiments were carried out to compare the proposed algorithm with traditional retrieval on a simulated dataset. In this dataset, documents, queries, and judgments of users were simulated. Exponential and Zipf distributions were used to build document collections. Uniform distribution was applied to build the queries. Zeta distribution was utilized to simulate the Bradford's law representing the judgments of users. Empirical results show a better performance of our algorithm compared with traditional retrieval.

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Section: Related Workmentioning

confidence: 99%

Probabilistic Reuse of Past Search Results

Gutiérrez-Soto

Hubert

2014

Lecture Notes in Computer Science

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“…These techniques require normalization of relevance scores among all systems. Probability-based: items are assigned a score based on the probability of relevance, according to a trained fusion system [85]. These techniques require training queries with corresponding ground truth (relevance judgements).…”

Section: Late Fusion Approachesmentioning

confidence: 99%

Retrieval of high-dimensional visual data: current state, trends and challenges ahead

Foncubierta–Rodríguez

Müller

Depeursinge

2013

Multimed Tools Appl

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Information retrieval algorithms have changed the way we manage and use various data sources, such as images, music or multimedia collections. First, free text information of documents from varying sources became accessible in addition to structured data in databases, initially for exact search and then for more probabilistic models. Novel approaches enable content-based visual search of images using computerized image analysis making visual image content searchable without requiring high quality manual annotations. Other multimedia data followed such as video and music retrieval, sometimes based on techniques such as extracting objects and classifying genre. 3D (surface) objects and solid textures have also been produced in quickly increasing quantities, for example in medical tomographic imaging. For these two types of 3D information sources, systems have become available to characterize the objects or textures and search for similar visual content in large databases. With 3D moving sequences (i.e., 4D), in particular medical imaging, even higherdimensional data have become available for analysis and retrieval and currently present many multimedia retrieval challenges. This article systematically reviews current techniques in various fields of 3D and 4D visual information retrieval and analyses the currently dominating application areas. The employed techniques are analysed and regrouped to highlight similarities and complementarities among them in order to guide the choice of optimal approaches for new 3D and 4D retrieval problems. Opportunities for future applications conclude the article. 3D or higher-dimensional visual information retrieval is expected to grow quickly in the coming years and in this respect this article can serve as a basis for designing new applications.

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“…Common data fusion algorithms like CombSUM, CombMNZ [4] and others [8,7] are widely used in text retrieval. The same algorithms can be applied to image retrieval domain.…”

Section: Related Workmentioning

confidence: 99%

Image Retrieval: Color and Texture Combining Based on Query-Image

Markov

Vassilieva²

2008

Lecture Notes in Computer Science

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It is a common way to process different image features independently in order to measure similarity between images. Color and texture are the common ones to use for searching in natural images. In [10] a technique to combine color and texture features based on a particular query-image in order to improve retrieval efficiency was proposed. Weighted linear combination of color and texture metrics was considered as a mixed-metrics. In this paper the mixed-metrics with different weights are compared to pure color and texture metrics and widely used CombMNZ data fusion algorithm. Experiments show that proposed metrics outperform CombMNZ method in some cases, and have close results in others.

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Probability-based fusion of information retrieval result sets

Cited by 9 publications

References 28 publications

Probabilistic Reuse of Past Search Results

Probabilistic Reuse of Past Search Results

Retrieval of high-dimensional visual data: current state, trends and challenges ahead

Image Retrieval: Color and Texture Combining Based on Query-Image

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