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dc.contributor.authorAlqasrawi, Yousef T. N.*
dc.contributor.authorNeagu, Daniel*
dc.contributor.authorCowling, Peter I.*
dc.date.accessioned2016-10-07T14:27:09Z
dc.date.available2016-10-07T14:27:09Z
dc.date.issued2013
dc.identifier.citationAlqasrawi Y, Neagu D and Cowling PI (2013) Fusing integrated visual vocabularies-based bag of visual words and weighted colour moments on spatial pyramid layout for natural scene image classification. Signal, Image and Video Processing. 7(4): 759-775.
dc.identifier.urihttp://hdl.handle.net/10454/9604
dc.descriptionNo
dc.description.abstractThe bag of visual words (BOW) model is an efficient image representation technique for image categorization and annotation tasks. Building good visual vocabularies, from automatically extracted image feature vectors, produces discriminative visual words, which can improve the accuracy of image categorization tasks. Most approaches that use the BOW model in categorizing images ignore useful information that can be obtained from image classes to build visual vocabularies. Moreover, most BOW models use intensity features extracted from local regions and disregard colour information, which is an important characteristic of any natural scene image. In this paper, we show that integrating visual vocabularies generated from each image category improves the BOW image representation and improves accuracy in natural scene image classification. We use a keypoint density-based weighting method to combine the BOW representation with image colour information on a spatial pyramid layout. In addition, we show that visual vocabularies generated from training images of one scene image dataset can plausibly represent another scene image dataset on the same domain. This helps in reducing time and effort needed to build new visual vocabularies. The proposed approach is evaluated over three well-known scene classification datasets with 6, 8 and 15 scene categories, respectively, using 10-fold cross-validation. The experimental results, using support vector machines with histogram intersection kernel, show that the proposed approach outperforms baseline methods such as Gist features, rgbSIFT features and different configurations of the BOW model.
dc.relation.isreferencedbyhttp://dx.doi.org/10.1007/s11760-011-0266-0
dc.subjectImage classification
dc.subject; Natural scenes
dc.subject; Bag of visual words
dc.subject; Integrated visual vocabulary
dc.subject; Pyramidal colour moments
dc.subject; Feature fusion
dc.subject; Semantic modelling
dc.subject; Local descriptors
dc.subject; Retrieval
dc.subject; Categorization
dc.subject; Codebooks
dc.subject; Representation
dc.subject; Recognition
dc.subject; Semantics
dc.subject; Object
dc.titleFusing integrated visual vocabularies-based bag of visual words and weighted colour moments on spatial pyramid layout for natural scene image classification
dc.status.refereedYes
dc.typeArticle
dc.type.versionNo full-text available in the repository


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