A nearest-neighbor approach to relevance feedback in content based image retrieval

Giacinto, Giorgio

doi:10.1145/1282280.1282347

Cited by 55 publications

(51 citation statements)

References 35 publications

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“…In this work we resort to a technique proposed in [7] where a score is assigned to each image of a database according to its distance from the nearest image belonging to the target class, and the distance from the nearest image belonging to a different class. This score is further combined to a score related to the distance of the image from the region of relevant images.…”

Section: K-nn Relevance Feedbackmentioning

confidence: 99%

ImageHunter: A Novel Tool for Relevance Feedback in Content Based Image Retrieval

Tronci

Murgia

Pili

et al. 2013

New Challenges in Distributed Information Filtering and Retrieval

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Nowadays, a very large number of digital image archives is easily produced thanks to the wide diffusion of personal digital cameras and mobile devices with embedded cameras. Thus, each personal computer, personal storage unit, as well as photo-sharing and social-network web sites, are rapidly becoming the repository for thousands, or even billions of images (i.e., more than 100 million photos are uploaded every day on the social site Facebook 1 ). As a consequence, there is an increasing need for tools enabling the semantic search, classification, and retrieval of images. The use of meta-data associated to images solves the problems only partially, as the process of assigning reliable meta data to images is not trivial, is slow, and closely related to whom performed the task. One solution for effective image search and retrieval is to combine content-based analysis with feedbacks from the users. In this paper we present Image Hunter, a tool that implements a Content Based Image Retrieval (CBIR) engine with a Relevance Feedback mechanism. Thanks to a user friendly interface the tool is especially suited to unskilled users. In addition, the modular structure permits the use of the same core both in web-based and stand alone applications.

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Section: K-nn Relevance Feedbackmentioning

confidence: 99%

ImageHunter: A Novel Tool for Relevance Feedback in Content Based Image Retrieval

Tronci

Murgia

Pili

et al. 2013

New Challenges in Distributed Information Filtering and Retrieval

Self Cite

View full text Add to dashboard Cite

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“…Each image is ranked according to a relevance score depending on nearest neighbor distances. This approach allows recalling a higher percentage of images with respect to SVM-based techniques [22] there after quotient space granularity computing theory into image retrieval field, clarify the granularity thinking in image retrieval, and a novel image retrieval method is imported. Firstly, aiming at the Different behaviors under different granularities, obtain color features under different granularities, achieve different quotient spaces; secondly, do the attribute combination to the obtained quotient spaces according to the quotient space granularity combination principle; and then realize image retrieval using the combined attribute function.…”

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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“…Every image is ranked according to a relavamce score depending on the nearest neighbor distance. The author has reported that this method outperforms the support vector machine [3]. Wei Bian et al proposed a biased discriminant Euclidean embedding which models the intraclass geometry and interclass discrimination and hence solves the undersampled problem [4].…”

Section: Introductionmentioning

confidence: 99%

A New Relevance Feedback based Approach for Efficient Image Retrieval

Karthik¹,

Saha²,

Chaithra³

2013

IJCA

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The rapid growth of digital image data summons the need for an effective and efficient content-based image searching system. Such systems should address the needs of the end user and should deliver the relevant images based on the search criteria. In order to meet this requirement, the content-based image search technique should capture the color and texture information. The performance of the algorithm can be enhanced using relevance feedback method. In this paper, a content-based image retrieval method based on image and its complement is presented. The similarity between the images is identified using an approach based on most significant highest priority (MSHP) principle or using a new distance measure which belongs to minkowski family. The retrieval rate is enhanced by relevance feedback technique based on kmeans algorithm. The approach is tested on Simplicity test dataset and a comparable performance was achieved

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A nearest-neighbor approach to relevance feedback in content based image retrieval

Cited by 55 publications

References 35 publications

ImageHunter: A Novel Tool for Relevance Feedback in Content Based Image Retrieval

ImageHunter: A Novel Tool for Relevance Feedback in Content Based Image Retrieval

A Survey On: “Content Based Image Retrieval Systems”

A New Relevance Feedback based Approach for Efficient Image Retrieval

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