Visual word spatial arrangement for image retrieval and classification

Penatti, Otávio Augusto Bizetto; Silva, Fernanda B.; Valle, Eduardo; Gouet-Brunet, Valérie; Torres, Ricardo da S.

doi:10.1016/j.patcog.2013.08.012

Cited by 74 publications

(50 citation statements)

References 34 publications

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“…1a such that the maximum possible number of occurrence 25 can be represented in the k-bit binary form. FP 2 (3,3) is computed by concatenating bp c, 2 (3, 3) for c = [1,5] which is binary representation of N c, 2 (3,3) . Similarly, FP 1 (3,3) is also computed in Fig.…”

Section: Local Neighbourhood-based Colour Occurrence Binary Patternmentioning

confidence: 99%

“…Broadly, image retrieval is categorised into three types according to its usability: text-based, content-based and semantic-based. In the early days, text-based approaches were being used for image retrieval but the scope of such methods got reduced on the existence of content-based image retrieval (CBIR) because retrieving images from its content are more visually accurate [1]. The main aim of CBIR is to facilitate efficient searching, browsing and matching over large databases either offline or online opening an active research area in the field of computer vision for more than decades.…”

Section: Introductionmentioning

confidence: 99%

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Local neighbourhood‐based robust colour occurrence descriptor for colour image retrieval

Dubey

Singh

2015

IET Image Processing

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Content-based image retrieval (CBIR) is demanding accurate with efficient retrieval approaches to index and retrieve the most similar images from the huge image databases. This study introduces a novel local neighbourhood-based robust colour occurrence descriptor (LCOD) to encode the colour information present in the local structure of the image. The colour information is processed in two steps: first, the number of colours is reduced into a less number of shades by quantising the red-green-blue colour space; second, the reduced colour shade information of the local neighbourhood is used to compute the descriptor. A local colour occurrence binary pattern is generated for each pixel of the image by representing each reduced colour shade occurrence in its local neighbourhood using a binary pattern. The descriptor is constructed by summing the local colour occurrence binary patterns of all the pixels in the image. LCOD is tested over the natural and colour texture databases for CBIR and experimental results suggest that LCOD outperforms other state-of-the-art descriptors. The performance of the proposed descriptor is promising in the case of illumination difference, rotation and scaling also and it can be effectively used for accurate image retrieval under various image transformations.

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Section: Local Neighbourhood-based Colour Occurrence Binary Patternmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Local neighbourhood‐based robust colour occurrence descriptor for colour image retrieval

Dubey

Singh

2015

IET Image Processing

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“…With the rapid development of imaging techniques, the amount of visual information has increased significantly, providing richer data sources for image tasks such as image annotation, recognition, retrieval, and classification (Carneiro et al, 2007;Penatti et al, 2014;Feng et al, 2017;Feng and Zhou, 2016). Scene classification is a precondition to achieve higher level scene interaction for robots.…”

Section: Introductionmentioning

confidence: 99%

A hierarchical inferential method for indoor scene classification

Jiang

Liu

et al. 2017

International Journal of Applied Mathematics and Computer Science

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Indoor scene classification forms a basis for scene interaction for service robots. The task is challenging because the layout and decoration of a scene vary considerably. Previous studies on knowledge-based methods commonly ignore the importance of visual attributes when constructing the knowledge base. These shortcomings restrict the performance of classification. The structure of a semantic hierarchy was proposed to describe similarities of different parts of scenes in a fine-grained way. Besides the commonly used semantic features, visual attributes were also introduced to construct the knowledge base. Inspired by the processes of human cognition and the characteristics of indoor scenes, we proposed an inferential framework based on the Markov logic network. The framework is evaluated on a popular indoor scene dataset, and the experimental results demonstrate its effectiveness.

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“…2) Image feature space quantization Penatti et al [9] proposed a spatial arrangement method of bag of words model to coding the spatial location of visual word by dividing the image into four quadrants, the spatial information was added effectively. Li et al [10] proposed a new multilevel structure of the image compression coding method, semantic and spatial levels were introduced in it.…”

Section: Introductionmentioning

confidence: 99%