Local patterns constrained image histograms for image retrieval

Fan, Zenghua; Li, Jilin; Bo, Wu; Wu, Yadong

doi:10.1109/icip.2008.4711911

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…Extracting information through combining local texture patterns with global image histogram, LPCIH is an effective image feature representation method with a flexible image segmentation process. This kind of feature representation is robust and invariant for several images transforms, such as rotation, scaling and damaging [17]. In another system the image is represented by a Fuzzy Attributed Relational Graph (FARG) that describes each object in the image, its attributes and spatial relation.…”

Section: Tandon Et Al Developed a Cbir System Called Fish-fastmentioning

confidence: 99%

Survey paper on Sketch Based and Content Based Image Retrieval

2015

IJSR

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This survey paper presents an overview of development of Sketch Based Image Retrieval (SBIR) and Content based image retrieval (CBIR) in the past few years. There is awful growth in bulk of images as well as the far-flung application in too many fields. The main attributes to represent as well index the images are color, shape, texture, spatial layout. These features of images are extracted to check similarity among the images. Generation of special query is the main problem of content based image retrieval. Features are extracted from the images and the similarity between them is check by using different algorithms. Sketch based image retrieval is effective and chief method which are not necessarily having a high skill to draw the query sketch. Over last twenty years different algorithms and models are explored for the retrieval of images. In concluding section we present the limitations of the current image retrieval algorithms.

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Section: Tandon Et Al Developed a Cbir System Called Fish-fastmentioning

confidence: 99%

Survey paper on Sketch Based and Content Based Image Retrieval

2015

IJSR

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“…However, it is time-cost, insensitive to position variance and ignoring some local visual information. While image retrieval method based on local features can speed searching and easily satisfy query user [10]. In [11] edge histogram descriptor combined with concept knowledge has been used to retrieve images with car related with a query.…”

Section: Introductionmentioning

confidence: 99%

Effective Scanned-Certification Image Retrieval Based on Local Object and Block Matching

Zhang¹,

Huang²,

Liu³

2014

JSW

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Scanned certifications are widely used in China as proof of past achievements. To avoid repetitive usage of the same certification in various awards or funding applications, we design and implement a scanned-certification image retrieval system based on local object and block matching. The seal is used as a salient feature and a modified Hough round detection method is applied for extraction. And local round seals are combined with blocks attributes of the seals to build image indexes. Experimental results will demonstrate the effectiveness of the system. Additionally, we conduct experiments on single color histogram, which further demonstrates the effectiveness of the proposed image retrieval method.

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“…This kind of feature representation is robust and invariant for several image transforms, such as rotation, scaling and damaging. [27]. In another system the image is represented by a Fuzzy Attributed Relational Graph (FARG) that describes each object in the image, its attributes and spatial relation.…”

Section: Recent Workmentioning

confidence: 99%

A Survey On: “Content Based Image Retrieval Systems”

Singhai¹,

Shandilya²

2010

IJCA

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As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. so nowadays the content based image retrieval are becoming a source of exact and fast retrieval. In this paper the techniques of content based image retrieval are discussed, analysed and compared. It also introduced the feature like neuro fuzzy technique, color histogram, texture and edge density for accurate and effective Content Based Image Retrieval System. General TermsContent Based Image Retrieval Technology, neuro-fuzzy system.

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Local patterns constrained image histograms for image retrieval

Cited by 9 publications

References 15 publications

Survey paper on Sketch Based and Content Based Image Retrieval

Survey paper on Sketch Based and Content Based Image Retrieval

Effective Scanned-Certification Image Retrieval Based on Local Object and Block Matching

A Survey On: “Content Based Image Retrieval Systems”

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