2016
DOI: 10.1155/2016/1861247
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Retrieval Architecture with Classified Query for Content Based Image Recognition

Abstract: The consumer behavior has been observed to be largely influenced by image data with increasing familiarity of smart phones and World Wide Web. Traditional technique of browsing through product varieties in the Internet with text keywords has been gradually replaced by the easy accessible image data. The importance of image data has portrayed a steady growth in application orientation for business domain with the advent of different image capturing devices and social media. The paper has described a methodology… Show more

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Cited by 14 publications
(9 citation statements)
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“…Das et al [9] proposed an approach where extracting the features of an image is achieved through image binarization. This enhances retrieval of images and its identification using content based image recognition.…”
Section: Feature Extraction and Content Based Image Retrieval For Higmentioning
confidence: 99%
“…Das et al [9] proposed an approach where extracting the features of an image is achieved through image binarization. This enhances retrieval of images and its identification using content based image recognition.…”
Section: Feature Extraction and Content Based Image Retrieval For Higmentioning
confidence: 99%
“…Das et al [8] described a method for extraction of features through binarization of images to enhance images retrieval and identification using content based image recognition. The authors tested their system using two public datasets with a sum of 3688 images.…”
Section: Introductionmentioning
confidence: 99%
“…Among existing ANN techniques, hashing approaches are proposed to map images to compact binary codes that approximately preserve the data structure in the original space [2][3][4][5][6]. Due to the high query speed and low memory cost, the hashing and image binarization techniques have become the most popular and effective techniques to enhance identification and retrieval of information using contentbased image recognition [4,[7][8][9][10][11][12][13][14][15][16]. Instead of real-valued features, images are represented by binary codes so that the time and memory costs of search can be greatly reduced [17].…”
Section: Introductionmentioning
confidence: 99%