A Survey of Content-Based Image Retrieval Systems

Veltkamp, Remco C.; Tanase, Mirela

doi:10.1007/978-1-4615-0987-5_5

Cited by 234 publications

(184 citation statements)

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“…[7,9,15,18] review the state of the art in segmentation, indexing and retrieval techniques in a number of CBIR systems. Despite increased work in aspects related to high level semantics of image features, the gap between low level image features and high level semantic expressions is a bottleneck in accessing multimedia data from databases.…”

Section: Related Workmentioning

confidence: 99%

“…In all such systems, image interpretation and understanding plays a vital role. Most of the research in this area is primarily based on use of low level image features like colour, texture, shape etc [9,18]. Although low level image processing algorithms and methodologies are quite mature, such systems are hard to be used effectively by a novice due to the semantic gap between user perception and understanding, and system requirements.…”

Section: Introductionmentioning

confidence: 99%

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Image retrieval through qualitative representations over semantic features

Qayyum

2013

Multimed Tools Appl

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We propose a qualitative knowledge-driven semantic modelling approach for image retrieval based on qualitative relations over local semantic concepts of images. The relative similarity of two images is proportional to their qualitative similarity. The similarity measure is calculated for each query by exploiting the notion of conceptual neighbourhood -a measure of closeness between qualitative relations. The approach is motivated by the need to perform semantic querying using qualitative relations and bridge the semantic gap between a human user and that of CBIR systems. Three qualitative representations (and several variants) and a corpus of 700 natural scene images have been used to evaluate the effectiveness of image retrieval using this approach.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image retrieval through qualitative representations over semantic features

Qayyum

2013

Multimed Tools Appl

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“…Image recommendation systems [7] are a specific branch in the field of visual content recommendation [8]. In fact, this type of recommendation takes great advantage of Content-Based Image Retrieval (CBIR) techniques [9], as they use the same methods, such as users' tagging [10] or semantic content analysis [11].…”

Section: Related Workmentioning

confidence: 99%

“…The metric chosen for the calculation of the distances, based on the weighted Minkowski distance, similar to other studies about visual perception and quality [36,37], is shown in (7).…”

Section: Distance Calculationmentioning

confidence: 99%

Social and Content Hybrid Image Recommender System for Mobile Social Networks

et al. 2012

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One of the advantages of social networks is the possibility to socialize and personalize the content created or shared by the users. In mobile social networks, where the devices have limited capabilities in terms of screen size and computing power, Multimedia Recommender Systems help to present the most relevant content to the users, depending on their tastes, relationships and profile. Previous recommender systems are not able to cope with the uncertainty of automated tagging and are knowledge domain dependant. In addition, the instantiation of a recommender in this domain should cope with problems arising from the collaborative filtering inherent nature (cold start, banana problem, large number of users to run, etc.). The solution presented in this paper addresses the abovementioned problems by proposing a hybrid image recommender system, which combines collaborative filtering (social techniques) with content-based techniques, leaving the user the liberty to give these processes a personal weight. It takes into account aesthetics and the formal characteristics of the images to overcome the problems of current techniques, improving the performance of existing systems to create a mobile social networks recommender with a high degree of adaptation to any kind of user.

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“…The most CBIR systems use the querying images by examples (Query-By-Example) approach to search for the most similar images to the given example image among a number of candidate images. Some of the CBIR systems are QBIC (Query by Image Content), Virage, Pichunter, VisualSEEK, Chabot, Excalibur, Photobook, Jacob, Digital Library Project [1].…”

Section: Introductionmentioning

confidence: 99%

Image Retrieval using Harris Corners and Histogram of Oriented Gradients

Velmurugan¹,

Baboo²

2011

IJCA

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Content based image retrieval is the technique to retrieve similar images from a database that are visually similar to a given query image. It is an active and emerging research field in computer vision. In our proposed system, the Interest points based Histogram of Oriented Gradients (HOG) feature descriptor is used to retrieve the relevant images from the database. The dimensionality of the HOG feature vector is reduced by Principle Component analysis (PCA). To improve the retrieval accuracy of the system the Colour Moments along with HOG feature descriptor are used in this system. The Interest points are detected using the Harris-corner detector in order to extract the image features. The KD-tree is used for matching and indexing the features of the query image with the database images.

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A Survey of Content-Based Image Retrieval Systems

Cited by 234 publications

References 0 publications

Image retrieval through qualitative representations over semantic features

Image retrieval through qualitative representations over semantic features

Social and Content Hybrid Image Recommender System for Mobile Social Networks

Image Retrieval using Harris Corners and Histogram of Oriented Gradients

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