Content-Based Image Retrieval Via Vector Quantization

Daptardar, Ajay H.; Storer, James A.

doi:10.1007/11595755_61

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…After codebook generation encoding process is done on query image. As a result 3 indices are represents each image vector where each index represents the best match codeword with respective to color [1,2] channel. After indexing histogram for each color is generated which is represented for RGB colorspace as Hr, Hg, Hb.…”

Section: Structure Of Codebookmentioning

confidence: 99%

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Content Based Vector Image Retrieval using LBG Algorithm

Shinde¹,

Mandal²,

Kulkarni³

et al. 2017

International Journal of Advanced Research in Computer and Comm

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There are many image retrieval and image compression techniques. Most of the image retrieval techniques are not capable to fastest relevant image retrieval. CBVIR is also known as Content based vector image retrieval or Query based image content. In CBIR, user gives image as an input to the system and after processing system returns similar images of Output. In this work, we present CBVIR using VQ. VQ is a classical quantization technique. VQ captures the spatial relationship between pixel. VQ contains codebook generation, encoding and decoding. Effective image indexing and retrieval technique are essential using codebook. Using CBVIR , retrieval performance is faster than the color based image retrieval. In this paper, we present a snapshot of the recent developed schemes. The discussed schemes include LBG, Enhance LBG (ELBG).

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Section: Structure Of Codebookmentioning

confidence: 99%

“…Here we are used query by example model [1], where user gives image as a input and system returns similar images as output. In CBVIR, each image that is stored in database that has its feature is extracted and compared with features of query images.…”

Section: Introductionmentioning

confidence: 99%

Content Based Vector Image Retrieval using LBG Algorithm

Shinde¹,

Mandal²,

Kulkarni³

et al. 2017

International Journal of Advanced Research in Computer and Comm

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“…There is no definite agreement on the selection of features for best results. The classifiers used vary from the rule and fuzzy rules based [9], support vector machines [13], [26]; ART [13], SOM [22], and vector quantization [27], [28]. The most popular and successful classifier described by many researchers has been the back propagation neural network [5], [6], [10], [12]- [14] which has been selected for evaluation of the different types of features.…”

Section: Previous Work and Problem Identificationmentioning

confidence: 99%

A Comparative Analysis of Feature Sets for Image Classification Using Back Propagation Neural Network

Husain¹

2015

IJIEE

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The exponential growth in image data over the internet has resulted in a growing need for searching images according to our requirements. Content based image retrieval systems extract similar images from databases or the internet for facilitation of their users. A number of different feature sets and classifiers have been used by researchers for content based image retrieval. The goal of this research is to evaluate some common features sets used for classification of images and identify the best features depending upon the user requirement. Some commonly used features have been studies and a set of six feature sets have been selected for evaluation by the Back-Propagation Neural Network (BPNN). The results have been evaluated on the basis of precision and recall and it can be concluded that for natural images none of the feature sets perform well universally on all classes and the selection of optimal feature set depends on the type/class of images.

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“…Database images are ranked based on this distortion, and experiments show that MDIR outperforms ALA in terms of retrieval precision, albeit at higher complexity. We introduced a similar approach using VQ codebooks and simple mean squared error (MSE) distortion [1]. Images are ranked based on the MSE when query features are encoded with database image codebooks.…”

Section: Introductionmentioning

confidence: 99%

VQ Based Image Retrieval Using Color and Position Features

Daptardar

Storer

2008

Data Compression Conference (Dcc 2008)

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We present a new lower complexity approach for content based image retrieval based on a relative compressibility similarity measure using VQ codebooks employing feature vectors based on color and position. In previous work we have developed a system that employs feature vectors that are a combination of color and position. In this paper, we present a new approach that decouples color and position. We present this approach as two methods. The first trains separate codebooks for color and position features, eliminating the need for potentially application specific feature weightings during training. The second method achieves nearly the same performance at greatly reduced complexity by partitioning images into regions and training high-rate TSVQ codebooks for each region (i.e., position information is made implicit). Features extracted from query regions are encoded with the corresponding database region codebooks. The maximum number of codewords that a database region codebook may contain is determined at runtime and is a function of the query features. Region codebooks are then pruned appropriately before encoding query features. Experiments performed on the COREL image database show this new approach to provide almost equivalent retrieval precision to our previous method of jointly trained codebooks (and an improvement over previous methods) at much lower complexity. BackgroundWith the recent proliferation of digital images, there is a need for information systems that can organize and store images using models which support content based queries. In the query-by-example setting (Eakins and Graham [4]), a user presents the system with a query image and the system responds by retrieving a set of database images with (visually) similar content. Given the discriminative power of color features and the simple histogram model, global color histograms which are relatively invariant to spatial transformations such as translation and rotation, have been effectively used

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Content-Based Image Retrieval Via Vector Quantization

Cited by 10 publications

References 14 publications

Content Based Vector Image Retrieval using LBG Algorithm

Content Based Vector Image Retrieval using LBG Algorithm

A Comparative Analysis of Feature Sets for Image Classification Using Back Propagation Neural Network

VQ Based Image Retrieval Using Color and Position Features

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