Region-based image retrieval using integrated color, shape, and location index

Prasad, B. G.; Biswas, K. K.; Gupta, Shivam

doi:10.1016/j.cviu.2003.10.016

Cited by 115 publications

(67 citation statements)

References 39 publications

(62 reference statements)

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“…This is the reason that the image representations obtained by the image segmentation are much more powerful than the global image representations. However, the drawback of the segmentation based image features is that currently there is no technique available that can perform the image segmentation in a better way, also the associated cost for image segmentation in terms of computational time makes it of no practical use [11,20,22,23].…”

Section: Introductionmentioning

confidence: 99%

“…are all examples of the global image features. Region representation or the local features are obtained either by the segmented regions [11,19,20] or by detecting the interest points, e.g., features obtained by scale-invariant feature transform (SIFT) [21] and sped up robust features (SURF) [14]. Segmentation allows to generate the representations that take into account the actual objects of interest and avoid image regions that are less informative.…”

Section: Introductionmentioning

confidence: 99%

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Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

Ashraf

Bashir

Irtaza

et al. 2015

Entropy

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One of the major requirements of content based image retrieval (CBIR) systems is to ensure meaningful image retrieval against query images. The performance of these systems is severely degraded by the inclusion of image content which does not contain the objects of interest in an image during the image representation phase. Segmentation of the images is considered as a solution but there is no technique that can guarantee the object extraction in a robust way. Another limitation of the segmentation is that most of the image segmentation techniques are slow and their results are not reliable. To overcome these problems, a bandelet transform based image representation technique is presented in this paper, which reliably returns the information about the major objects found in an image. For image retrieval purposes, artificial neural networks (ANN) are applied and the performance of the system and achievement is evaluated on three standard data sets used in the domain of CBIR.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

Ashraf

Bashir

Irtaza

et al. 2015

Entropy

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“…For shape feature, we will adopt the region-based shape representation proposed in Lu & Sajjanhar (1999) and Prasad et al (2004) with slight modification. The region based method can be illustrated in Figure 10, where a grid with fixed-size square cells was used to cover the shape region.…”

Section: Image Boundary Angle Codebookmentioning

confidence: 99%

“…Shape based image retrieval are divided into two categories: boundary-based and region-based Safar et al (2000). Among several different approaches, the grid-based method Lu & Sajjanhar (1999), which belongs to region-based methods, was later used by Prasad et al (2004). There are several factors that make shape-based image retrieval very challenging and that have limited development of shape-based image retrieval.…”

mentioning

confidence: 99%

Tile-based Image Visual Codewords Extraction for Efficient Indexing and Retrieval

Zhang

Nasraoui

2010

Pattern Recognition Recent Advances

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“…As fusion weights were not optimum, the effectiveness of retrieval performance was inconsistent. Prasad et al [6] proposed a technique to retrieve images by region matching using a combined feature index based on color, shape, and location within the framework of MPEG-7. Simulation results show that the image retrieval was done only based on dominant regions within each image and hence the retrieval performance was incomplete.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Bee Colony Optimization Algorithm for Image Classification Using Color and Texture Feature Similarity Fusion

Chandrakala¹,

Sumathi

2012

ISRN Artificial Intelligence

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With the advancement in image capturing device, the image data is being generated in high volumes. The challenging and important problem in image mining is to reveal useful information by grouping the images into meaningful categories. Image retrieval is extensively required in recent decades because CBIR is regarded as one of the most effective ways of accessing visual data. Conventionally, the way of searching the collections of digital image database is by matching keywords with image caption, descriptions and labels. Keyword based searching method provides very high computational complexity and user has to remember the exact keywords used in the image database. Instead, our paper proposes image retrieval system with Artificial Bee Colony optimization algorithm by fusing similarity score based on color and texture features of an image thereby achieving very high classification accuracy and minimum retrieval time. In this scheme, the color is described by color histogram method in HSV space and texture represented by contrast, energy, entropy, correlation and local stationary over the region in an image. The proposed Comprehensive Image Retrieval scheme fuses the color and texture feature based similarity score between query and all the database images. The experimental results show that the proposed method is superior to keywords based retrieval and content based retrieval schemes with individual low-level features of image.

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Region-based image retrieval using integrated color, shape, and location index

Cited by 115 publications

References 39 publications

Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

Tile-based Image Visual Codewords Extraction for Efficient Indexing and Retrieval

Application of Artificial Bee Colony Optimization Algorithm for Image Classification Using Color and Texture Feature Similarity Fusion

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