Fast Planar-Oriented Ripple Search Algorithm for Hyperspace VQ Codebook

Chang, Chin‐Chen; Wu, Wen-Chuan

doi:10.1109/tip.2007.894256

Cited by 62 publications

(39 citation statements)

References 20 publications

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“…Vector Quantization is used in many applications [10], such as speech recognition and face detection [11], [12], Image segmentation [13], speech data compression [14], Contained Based Image Retrieval (CBIR) [15], also in iris recognition [16] etc.…”

Section: Literature Survey 21 Vector Quantization(vq)mentioning

confidence: 99%

Novel Keyframe Extraction for Video Content Summarization using LBG Codebook Generation Technique of Vector Quantization

Thepade¹,

Patil²

2015

IJCA

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In the current era, most of the digital information in the form of multimedia with a giant share of videos. Videos do have audio and visual content where the visual content has number of frames put in a sequence. Most of the consecutive frames do have very little discriminative contents. In video summarization process, several frames containing similar information are needed to get processed. This leads to redundant slow processing speed and complexity, time consumption. Video summarization using key frames can ease the speedup of video processing. In this paper, novel key frame extraction method is proposed with Linde-Buzo-Gray (LBG) codebook generation techniques of vector quantization with ten different codebook sizes. Experimentation done with the help of the test bed of videos has shown that higher codebook sizes of LBG have given better completeness in key frame extraction for video summarization. Experimental results are also discussed to represent the validity of the proposed method for video content summarization.

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Section: Literature Survey 21 Vector Quantization(vq)mentioning

confidence: 99%

Novel Keyframe Extraction for Video Content Summarization using LBG Codebook Generation Technique of Vector Quantization

Thepade¹,

Patil²

2015

IJCA

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“…Vector Quantization (VQ) was initially developed and implemented for image compression, with the help of many codebook generation and quantization algorithms [25,26,27,28], but now a days it has been extensively use in other applications, such as image segmentation [29], speech recognition [30], pattern recognition and face detection [31,32], tumor demarcation in MRI and Mammogram images [33,34], content based image retrieval [35,36] etc. In this paper, this method has been used as clustering aid in demarcation of area of interest (cysts and tumor) in breast ultrasound images.…”

Section: Vector Quantizationmentioning

confidence: 99%

Segmentation of Ultrasound Breast Images using Vector Neighborhood with Vector Sequencing on KMCG and augmented KMCG algorithms

Kekre¹,

Shrinath²

2013

IJACSA

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Abstract-B mode ultrasound (US) imaging is popular and important modality to examine the range of clinical problems and also used as complimentary to the mammogram imaging to detect and diagnose the nature breast tumor. To understand the nature (benign or malignant) of the tumor most of the radiologists focus on shape and boundary. Therefore boundary is as important characteristic of the tumor along with the shape. Tracing the contour manually is a time consuming and tedious task. Automated and efficient segmentation method also helps radiologists to understand and observe the volume of a tumor (growth or shrinkage). Inherent artifact present in US images, such as speckle, attenuation and shadows are major hurdles in achieving proper segmentation. Along with these artifacts, inhomogeneous texture present in the region of interest is also a major concern. Most of the algorithms studies in the literature include noise removal technique as a preprocessing step. Here in this paper, we are eliminating this step and directly handling the images with high degree of noise. VQ based clustering technique is proposed for US image segmentation with KMCG and augmented KMCG codebook generation algorithms. Using this algorithm images are divided in to clusters, further these clusters are merged sequentially. A novel technique of sequential cluster merging with vector sequencing has been used. We have also proposed a technique to find out the region of interest from the selected cluster with seed vector acquisition.Results obtained by our method are compared with our earlier method and Marker Controlled Watershed transform. With the opinion of the expert radiologist, we found that our method gives better results.

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“…Vector Quantization (VQ) [17][18][19][20][21][22][23][24][25] is an efficient technique for data compression and has been successfully used in various applications such as index compression [26,27]. VQ has been very popular in a variety of research fields such as speech recognition and face detection [28,29].…”

Section: Vector Quantization:-mentioning

confidence: 99%

"Performance Comparison of LBG, KPE, KFCG and KMCG for Global Codebook Technique"

Kekre¹,

Tanuja²,

Raut³

et al. 2011

IJCA

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Vector quantization is a classical quantization technique from signal processing which allows the modeling of probability density functions by the distribution of prototype vectors. It was originally used for data compression. It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them. Each group is represented by its centroid point, as in clustering algorithms.Vector Quantization is a technique of compressing data based on grouping blocks having similar data. These blocks are called Code Vectors and all the code vectors grouped together is called a Codebook. The key to VQ data compression is a good codebook. In order to reduce bandwidth overhead it is necessary to generate Global Codebook for a particular class of images. Otherwise local codebook has to be transferred every time before the transmission of image. In this paper various global codebook generation algorithms for vector quantization for color images are presented.

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Fast Planar-Oriented Ripple Search Algorithm for Hyperspace VQ Codebook

Cited by 62 publications

References 20 publications

Novel Keyframe Extraction for Video Content Summarization using LBG Codebook Generation Technique of Vector Quantization

Novel Keyframe Extraction for Video Content Summarization using LBG Codebook Generation Technique of Vector Quantization

Segmentation of Ultrasound Breast Images using Vector Neighborhood with Vector Sequencing on KMCG and augmented KMCG algorithms

"Performance Comparison of LBG, KPE, KFCG and KMCG for Global Codebook Technique"

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