Efficient and effective Querying by Image Content

Faloutsos, Christos; Barber, Ronald; Flickner, Myron; Hafner, James Lee; Niblack, W.; Petković, Dragutin; Equitz, W.

doi:10.1007/bf00962238

Cited by 1,033 publications

(510 citation statements)

References 30 publications

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“…As far as the scope of features is concerned, features can be divided into two main categories: global features and local features. Global feature category includes color histogram [3,4], texture histogram [7], color layout [5] of the whole image, and features selected from multidimensional discriminant analysis of a collection of images [23]. While local feature category includes color, texture [9], and shape features for sub images, segmented regions [2,6], or interest points.…”

Section: Related Workmentioning

confidence: 99%

Correlated Networks for Content Based Image Retrieval

Irtaza¹,

Jaffar²,

Aleisa³

2013

IJCIS

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Efficient CBIR systems are based on three things, (1) how they represent the repository images in the form of signature; (2) how they measure the similarity of the database images with query image, (3) how they retrieve the semantically similar images in response of a query image. The paper is focusing on these three things. For signature development, curvelet transform, wavelet packets, and Gabor filters based signature development is introduced. For measuring the similarity, Pearson correlation is used as a distance measure; and for retrieving the semantically similar images in response of query images, Neural Network based architecture for content based image retrieval is presented. These things ensure the retrieval of images in a robust way. To elaborate the effectiveness of the presented work, the proposed method is compared with several existing CBIR systems, and it is proved that the proposed method has performed better than all comparative systems.

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Section: Related Workmentioning

confidence: 99%

Correlated Networks for Content Based Image Retrieval

Irtaza¹,

Jaffar²,

Aleisa³

2013

IJCIS

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“…In semantic approach, a hierarchical image [20] classification is done. Although many visual information systems have been developed recently [4], [7], [21], none of these systems operate by considering knowledge extracted from image repositories. In this paper, first I divide the whole image databases into small number of subgroups in accordance with the image semantics i.e.…”

Section: Semanticsmentioning

confidence: 99%

Integrating Pixel Cluster Indexing, Histogram Intersection And Discrete Wavelet Transform Methods For Color Images Content Based Image Retrieval System

Bhattacharjee¹

2010

IJCEE

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“…In applications like Query By Image Content (QBIC) [19], a user specifies the relative contributions of score components such as color, texture, etc. Each component assigns a score to each record (image) with respect to the query at hand.…”

Section: Motivationmentioning

confidence: 99%

Efficiency-Quality Tradeoffs for Vector Score Aggregation

Singitham

Mahabhashyam

Raghavan³

2004

Proceedings 2004 VLDB Conference

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Finding the nearest neighbors to a query in a vector space is an important primitive in text and image retrieval. Here we study an extension of this problem with applications to XML and image retrieval: we have multiple vector spaces, and the query places a weight on each space. Match scores from the spaces are weighted by these weights to determine the overall match between each record and the query; this is a case of score aggregation. We study approximation algorithms that use a small fraction of the computation of exhaustive search through all records, while returning nearly the best matches. We focus on the tradeoff between the computation and the quality of the results. We develop two approaches to retrieval from such multiple vector spaces. The first is inspired by resource allocation. The second, inspired by computational geometry, combines the multiple vector spaces together with all possible query weights into a single larger space. While mathematically elegant, this abstraction is intractable for implementation. We therefore devise an approximation of this combined space. Experiments show that all our approaches (to varying extents) enable retrieval quality comparable to exhaustive search, while avoiding its heavy computational cost.

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Efficient and effective Querying by Image Content

Cited by 1,033 publications

References 30 publications

Correlated Networks for Content Based Image Retrieval

Correlated Networks for Content Based Image Retrieval

Integrating Pixel Cluster Indexing, Histogram Intersection And Discrete Wavelet Transform Methods For Color Images Content Based Image Retrieval System

Efficiency-Quality Tradeoffs for Vector Score Aggregation

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