Multiscale directional filter bank with applications to structured and random texture retrieval

Cheng, Kin-On; Law, Ngai-Fong; Siu, Wan-Chi

doi:10.1016/j.patcog.2006.07.014

Cited by 25 publications

(21 citation statements)

References 24 publications

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“…A modification to the PDFB was proposed, namely the multiscale directional filter bank (MDFB) that is redundant in nature and a number of possible structures are available based on the choice of lowpass filter and the number of directional decomposition. According to Cheng et al (2007a), MDFB introduces an additional decomposition in the high-frequency band and thereby improves the radial frequency resolution at a cost of one set of extra scale and directional decompositions on the full image size. This results in increased number of computations.…”

Section: Fast Multiscale Direction Filter Banks (Fmdfb)mentioning

confidence: 99%

“…Also, MDFB has a higher redundancy than PDFB. The over completeness and increased frequency resolution of MDFB was found useful in applications like texture characterization and retrieval (Cheng et al, 2007a). Cheng et al (2007b) proposed a fast and reduced redundancy structure for this MDFB (FMDFB).…”

Section: Fast Multiscale Direction Filter Banks (Fmdfb)mentioning

confidence: 99%

“…Fast multiscale directional filter bank is an image representation scheme that is successfully used in feature extraction and image enhancement (Cheng, Law, & Siu, 2007a), ( Cheng, Law, & Siu, 2007b). In our previous work, an image denoising scheme with FMDFB is proposed that works well for additive Gaussian and multiplicative speckle noise removal compared to other conventional multiscale denoising methods such as wavelet 803 and contourlet based denoising (Leavline, Sutha, & Singh, 2014a).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis and Modeling of Statistical Properties of FMDFB Subband Coefficients

Leavline

Sutha²

2016

J. Mod. App. Stat. Meth.

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Section: Fast Multiscale Direction Filter Banks (Fmdfb)mentioning

confidence: 99%

Section: Fast Multiscale Direction Filter Banks (Fmdfb)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis and Modeling of Statistical Properties of FMDFB Subband Coefficients

Leavline

Sutha²

2016

J. Mod. App. Stat. Meth.

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“…The MDFB alter the pyramidal directional filter bank or Contourlet Transform by introducing scale decomposition in its finest scale at additional computational complexity [5]. Cheng, et al, [6] proposed a fast structure for this MDFB named as fast multiscale directional filter banks (FMDFB).…”

Section: Introductionmentioning

confidence: 99%

Design of FIR Filters for Fast Multiscale Directional Filter Banks

Leavline¹,

Sutha²

2014

IJUNESST

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“…of the query image and the images stored in the database. Although different image characterization methods [2], [3] have been explored to represent images with basic low-level features but their usefulness is limited by the gap, between low-level features and high-level concepts known as semantic gap. Performance of CBIR is still far from user's expectations owing to semantic gap.…”

mentioning

confidence: 99%

Interactive Image Retrieval in a Fuzzy Framework

Kundu

Banerjee

Bagrecha

2009

Fuzzy Logic and Applications

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Abstract. In this paper, an interactive image retrieval scheme using MPEG-7 visual descriptors is proposed. The performance of image retrieval systems is still limited due to semantic gap, which is created from the discrepancies between the computed low-level features (color, texture, shape, etc.) and user's conception of an image. As a result, more interest has been created towards development of efficient learning mechanism other than designing sophisticated low-level feature extraction algorithms. A simple relevance feedback mechanism is proposed, that learns user's interest and updates feature weights based on a fuzzy feature evaluation measure. This has an advantage of handling comparatively small number of samples over those using standard classifiers involving large number of training samples and having more complexity. Extensive experiments have been performed to test to what extent the performance of an image retrieval system can be enhanced further using MPEG-7 standard visual features at minimum cost.Efficient image retrieval techniques from a large database have become an active field of research with the advent of the World-Wide Web. Content-Based Image Retrieval(CBIR) techniques are becoming more important with this basic requirement [1]. It is aimed at retrieving relevant images from an image database by measuring similarity between the automatically derived low-level features (color, texture, shape, etc. ) of the query image and the images stored in the database. Although different image characterization methods [2], [3] have been explored to represent images with basic low-level features but their usefulness is limited by the gap, between low-level features and high-level concepts known as semantic gap. Performance of CBIR is still far from user's expectations owing to semantic gap.To facilitate effective use of audio, visual(color, texture, shape, etc.) and motion descriptors, ISO/IEC has launched MPEG-7 to address multimedia retrieval. It provides a collection of specific, standard descriptors [4] used as a benchmark for evaluation of new schemes for image retrieval [5]. Among various state-of-the-art techniques in narrowing down the semantic gap, relevance feedback mechanism has been identified as an essential tool to provide significant performance boost in CBIR systems [6], [7], [8], through continuous learning and interaction with

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Multiscale directional filter bank with applications to structured and random texture retrieval

Cited by 25 publications

References 24 publications

Analysis and Modeling of Statistical Properties of FMDFB Subband Coefficients

Analysis and Modeling of Statistical Properties of FMDFB Subband Coefficients

Design of FIR Filters for Fast Multiscale Directional Filter Banks

Interactive Image Retrieval in a Fuzzy Framework

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