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Global semantic classification of scenes using power spectrum templates
IM'99 Proceedings of the 1999 international conference on Challenge of Image Retrieval
Image classification for content-based indexing
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Learning ontology for personalized video retrieval
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MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Adaptively Combining Local with Global Information for Natural Scenes Categorization
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Object re-detection using SIFT and MPEG-7 color descriptors
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Semantic modeling of natural scenes based on contextual Bayesian networks
Pattern Recognition
A relational vector space model using an advanced weighting scheme for image retrieval
Information Processing and Management: an International Journal
Modulating Shape Features by Color Attention for Object Recognition
International Journal of Computer Vision
Bag of spatio-visual words for context inference in scene classification
Pattern Recognition
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This paper presents a novel approach for visual scene representation, combining the use of quantized color and texture local invariant features (referred to here as visterms) computed over interest point regions. In particular we investigate the different ways to fuse together local information from texture and color in order to provide a better visterm representation. We develop and test our methods on the task of image classification using a 6-class natural scene database. We perform classification based on the bag-of-visterms (BOV) representation (histogram of quantized local descriptors), extracted from both texture and color features. We investigate two different fusion approaches at the feature level: fusing local descriptors together and creating one representation of joint texture-color visterms, or concatenating the histogram representation of both color and texture, obtained independently from each local feature. On our classification task we show that the appropriate use of color improves the results w.r.t. a texture only representation.