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Compact color–texture description for texture classification
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
Department of Information and Computer Science, Aalto University School of Science, Finland.
Computer Vision Center, CS Dept. Universitat Autonoma de Barcelona, Spain.
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.ORCID-id: 0000-0002-6096-3648
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2015 (Engelska)Ingår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 51, s. 16-22Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Describing textures is a challenging problem in computer vision and pattern recognition. The classification problem involves assigning a category label to the texture class it belongs to. Several factors such as variations in scale, illumination and viewpoint make the problem of texture description extremely challenging. A variety of histogram based texture representations exists in literature. However, combining multiple texture descriptors and assessing their complementarity is still an open research problem. In this paper, we first show that combining multiple local texture descriptors significantly improves the recognition performance compared to using a single best method alone. This gain in performance is achieved at the cost of high-dimensional final image representation. To counter this problem, we propose to use an information-theoretic compression technique to obtain a compact texture description without any significant loss in accuracy. In addition, we perform a comprehensive evaluation of pure color descriptors, popular in object recognition, for the problem of texture classification. Experiments are performed on four challenging texture datasets namely, KTH-TIPS-2a, KTH-TIPS-2b, FMD and Texture-10. The experiments clearly demonstrate that our proposed compact multi-texture approach outperforms the single best texture method alone. In all cases, discriminative color names outperforms other color features for texture classification. Finally, we show that combining discriminative color names with compact texture representation outperforms state-of-the-art methods by 7.8%,4.3%7.8%,4.3% and 5.0%5.0% on KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets respectively.

Ort, förlag, år, upplaga, sidor
Elsevier, 2015. Vol. 51, s. 16-22
Nyckelord [en]
Texture features; Color features; Texture classification; Image classification
Nationell ämneskategori
Elektroteknik och elektronik Datorseende och robotik (autonoma system)
Identifikatorer
URN: urn:nbn:se:liu:diva-111508DOI: 10.1016/j.patrec.2014.07.020ISI: 000345687500003OAI: oai:DiVA.org:liu-111508DiVA, id: diva2:756961
Tillgänglig från: 2014-10-20 Skapad: 2014-10-20 Senast uppdaterad: 2018-01-11Bibliografiskt granskad

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Khan, Fahad ShahbazFelsberg, Michael

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DatorseendeTekniska högskolanCentrum för medicinsk bildvetenskap och visualisering, CMIV
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Pattern Recognition Letters
Elektroteknik och elektronikDatorseende och robotik (autonoma system)

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