Using a Markov Random Field for Image Re-ranking Based on Visual and Textual Features
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| Format: | Artículo científico |
| Language: | en |
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Instituto Politécnico Nacional
2011
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| _version_ | 1866811791479144448 |
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| author | R. Omar Chávez |
| author_facet | R. Omar Chávez |
| contents | Using a Markov Random Field for Image Re-ranking Based on Visual and Textual Features R. Omar Chávez Manuel Montes L. Enrique Sucar Computación ranking Image Re Image Retrieval Relevance Feedback Markov Random Field We propose a novel method to re-order the list of images returned by an image retrieval system (IRS). The method combines the original order obtained by the IRS, the similarity between images obtained with visual and textual features, and a relevance feedback approach, all of them with the purpose of separating relevant from irrelevant images, and thus, obtaining a more appropriate order. The method is based on a Markov random field (MRF) model, in which each image in the list is represented as a random variable that could be relevant or irrelevant. The energy function proposed for the MRF combines two factors: the similarity between the images in the list (internal similarity); and information obtained from the original order and the similarity of each image with the query (external similarity). Experiments were conducted with resources from the Image CLEF 2008 forum for the photo retrieval track, taking into account textual and visual features. The results show that the proposed method improves, according to the MAP measure, the order of the original list up to 63% (in the textual case) and up to 55% (in the visual case); and suggest future work using a combination of both kind of features. 2011 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61520767006 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.4 Vol.14 |
| format | Artículo científico |
| id | redalyc_61520767006 |
| language | en |
| publishDate | 2011 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | Using a Markov Random Field for Image Re-ranking Based on Visual and Textual Features R. Omar Chávez Computación ranking Image Re Image Retrieval Relevance Feedback Markov Random Field Using a Markov Random Field for Image Re-ranking Based on Visual and Textual Features R. Omar Chávez Manuel Montes L. Enrique Sucar Computación ranking Image Re Image Retrieval Relevance Feedback Markov Random Field We propose a novel method to re-order the list of images returned by an image retrieval system (IRS). The method combines the original order obtained by the IRS, the similarity between images obtained with visual and textual features, and a relevance feedback approach, all of them with the purpose of separating relevant from irrelevant images, and thus, obtaining a more appropriate order. The method is based on a Markov random field (MRF) model, in which each image in the list is represented as a random variable that could be relevant or irrelevant. The energy function proposed for the MRF combines two factors: the similarity between the images in the list (internal similarity); and information obtained from the original order and the similarity of each image with the query (external similarity). Experiments were conducted with resources from the Image CLEF 2008 forum for the photo retrieval track, taking into account textual and visual features. The results show that the proposed method improves, according to the MAP measure, the order of the original list up to 63% (in the textual case) and up to 55% (in the visual case); and suggest future work using a combination of both kind of features. 2011 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61520767006 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.4 Vol.14 |
| title | Using a Markov Random Field for Image Re-ranking Based on Visual and Textual Features |
| topic | Computación ranking Image Re Image Retrieval Relevance Feedback Markov Random Field |
| url | https://www.redalyc.org/articulo.oa?id=61520767006 |