Content Based Image Retreival Using Fusion of Gabor Magnitude and Modified Block Truncation Coding

Kekre, H. B.; Bharadi, Vinayak Ashok; Thepade, Sudeep D.; Mishra, Bhogendra; Ghosalkar, S.; Sawant, Swati

doi:10.1109/icetet.2010.166

Cited by 14 publications

(13 citation statements)

References 11 publications

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“…The small blocks are coded one at a time. For each block, the original pixels within the block are coded using a binary bitmap the same size as the original block and two mean pixel values [4]. The method first computes the mean pixel value of the whole block and then each pixel in that block is compared to the block mean.…”

Section: A Modified Block Truncation Codingmentioning

confidence: 99%

“…uppermean red (Cr1), uppermean green (Cg1), uppermean blue (Cb1) and lowermean red (Cr2), lowermean (Cg2), lowermean blue (Cb2) [4], [5]. ii.…”

Section: B Pattern Generation For Texturementioning

confidence: 99%

“…But the major drawback of text based image retrieval are, annotation depends on the person who adds it. [4] Due to all these drawbacks, Content Based Image Retrieval is introduced.…”

Section: Introductionmentioning

confidence: 99%

“…[4]. Gabor filters are a group of wavelets, with each wavelet capturing energy at a specific frequency and a specific direction.…”

Section: Introductionmentioning

confidence: 99%

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Satellite Image Retrieval using Modified Block Truncation Coding and Kekre Transform Patterns

Sawant¹,

Bharadi²,

Kekre³

2013

SpecialIssue

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Abstract--Satellite images and Content Based Image Retrieval are interesting topics of research since years. Specifically, CBIR is on developing technologies for bridging the semantic gap that currently prevents wide-deployment of image content-based search engines. Image search engines currently in use are mostly rely on human generated data, such as text. Annotation of an image is totally depend on the person's perception who is going to store it into database. It is time-consuming as well as error prone. Therefore search engine using text input results in various nonrelevant images. To overcome drawbacks of text based image retrieval, Content based image retrieval is introduced where retrieval of images is totally depend on the features of images. Mostly, content-based methods are based on low-level descriptions, while high-level or semantic descriptions are beyond current capabilities. In this paper, we will try to implement the technique to fill this gap. This technique can eventually be extended to allow for content-based similarity type of search, to find out different query blocks from satellite image. We will fire an object as a query and most similar blocks will get retrieved. This will help out to trace particular object from large satellite image.

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Section: A Modified Block Truncation Codingmentioning

confidence: 99%

“…uppermean red (Cr1), uppermean green (Cg1), uppermean blue (Cb1) and lowermean red (Cr2), lowermean (Cg2), lowermean blue (Cb2) [4], [5]. ii.…”

Section: B Pattern Generation For Texturementioning

confidence: 99%

“…But the major drawback of text based image retrieval are, annotation depends on the person who adds it. [4] Due to all these drawbacks, Content Based Image Retrieval is introduced.…”

Section: Introductionmentioning

confidence: 99%

“…[4]. Gabor filters are a group of wavelets, with each wavelet capturing energy at a specific frequency and a specific direction.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Satellite Image Retrieval using Modified Block Truncation Coding and Kekre Transform Patterns

Sawant¹,

Bharadi²,

Kekre³

2013

SpecialIssue

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show abstract

“…The advantage of textual indexing of image is that it can provide user with key word searching, catalogue browsing and even with query interface. But the major drawback of text based image retrieval are, annotation depends on the person who adds it , the user of a Text Based Image Retrieval must describe an image using nearly the same keywords that were used by the annotator in order to retrieve that image [4]. Due to all these drawbacks, Content Based Image Retrieval is introduced.…”

Section: Introductionmentioning

confidence: 99%

Color &Texture Based Image Retrieval using Fusion of Modified Block Truncation Coding (MBTC) and Kekre Transform Patterns

Sawant¹,

Bharadi²,

Markarkandy³

2012

SpecialIssue

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-Content Based Image Retrieval is an interesting topic of research. This paper is about image content-based image search. Specifically, it is on developing technologies for bridging the semantic gap that currently prevents wide-deployment of image content-based search engines. Mostly, content-based methods are based on low-level descriptions, while high-level or semantic descriptions are beyond current capabilities. In this paper a CBIR is proposed based on color & texture based search. Modified Block Truncation Coding (MBTC) is used for color information retrieval. To extract texture information we are using pattern generated by transforms, currently we are considering Kekre Transform. The feature vector is generated by fusion of above mentioned techniques.

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Fusion Based Image Retrieval Using Local and Global Descriptor

Shendre¹,

Damahe²,

Thakur³

2018

Communications in Computer and Information Science

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Content Based Image Retreival Using Fusion of Gabor Magnitude and Modified Block Truncation Coding

Cited by 14 publications

References 11 publications

Satellite Image Retrieval using Modified Block Truncation Coding and Kekre Transform Patterns

Satellite Image Retrieval using Modified Block Truncation Coding and Kekre Transform Patterns

Color &Texture Based Image Retrieval using Fusion of Modified Block Truncation Coding (MBTC) and Kekre Transform Patterns

Fusion Based Image Retrieval Using Local and Global Descriptor

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