Enhanced Generic Fourier Descriptors for object-based image retrieval

Zhang, Dengsheng; Lu, Guojun

doi:10.1109/icassp.2002.5745451

Cited by 41 publications

(27 citation statements)

References 2 publications

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“…Some of the recently proposed descriptors fall in the contour-based category, as the curvature scale space (CSS) descriptor [8] (which has been standardized in the MPEG-7 framework) and the shape context matrices [1]. Some others belong to the class of region-based methods, like the descriptors based on moments (geometric [3], Zernike and Legendre [11]), on region frequency representations (Fourier descriptors [12]), on medial axis transform [9] and on shock graphs [10]. The goal of this paper is to develop a curve descriptor that satisfies the six principles set by MPEG-7 and a few other requirements, such as being rotation-scaling-translation (RST) invariant, having a clear physical interpretation and being easily extendable to the 3D case (for surface description).…”

Section: Introductionmentioning

confidence: 99%

Drums and Curve Descriptors

Zuliani

Kenney²,

Bhagavathy³

et al. 2004

Procedings of the British Machine Vision Conference 2004

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In this paper we present a new physically motivated curve descriptor based on the solution of Helmholtz's equation. The descriptor satisfies the six principles set by MPEG-7: it has a good retrieval accuracy, it is compact, it can be applied in general contexts, it has a reasonable computational complexity, it is robust and provides an hierarchical representation of the curve from coarse to fine. Moreover this descriptor generalizes straightforwardly to three dimensional surfaces. We tested the performance of the descriptor in the context of affine invariant curve matching using the multiview curve dataset (MCD), which consists of 40 curves (extracted from the MPEG-7 shape dataset) imaged under 14 different points of view. The results we obtained show that the proposed descriptor satisfies the MPEG-7 requirements and presents some advantages over some of the commonly used curve descriptors.

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Section: Introductionmentioning

confidence: 99%

Drums and Curve Descriptors

Zuliani

Kenney²,

Bhagavathy³

et al. 2004

Procedings of the British Machine Vision Conference 2004

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show abstract

“…To overcome the drawbacks of existing shape representation techniques, MGFD has been proposed [36,37]. Applying 2-D FT on a shape image in Cartesian space directly to derive FD is not practical because the features captured by 2-D FT are not rotation invariant.…”

Section: Shape Featuresmentioning

confidence: 99%

Optimal Query-Based Relevance Feedback in Medical Image Retrieval Using Score Fusion-Based Classification

Behnam

Pourghassem

2014

J Digit Imaging

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In this paper, a new content-based medical image retrieval (CBMIR) framework using an effective classification method and a novel relevance feedback (RF) approach are proposed. For a large-scale database with diverse collection of different modalities, query image classification is inevitable due to firstly, reducing the computational complexity and secondly, increasing influence of data fusion by removing unimportant data and focus on the more valuable information. Hence, we find probability distribution of classes in the database using Gaussian mixture model (GMM) for each feature descriptor and then using the fusion of obtained scores from the dependency probabilities, the most relevant clusters are identified for a given query. Afterwards, visual similarity of query image and images in relevant clusters are calculated. This method is performed separately on all feature descriptors, and then the results are fused together using feature similarity ranking level fusion algorithm. In the RF level, we propose a new approach to find the optimal queries based on relevant images. The main idea is based on density function estimation of positive images and strategy of moving toward the aggregation of estimated density function. The proposed framework has been evaluated on ImageCLEF 2005 database consisting of 10,000 medical X-ray images of 57 semantic classes. The experimental results show that compared with the existing CBMIR systems, our framework obtains the acceptable performance both in the image classification and in the image retrieval by RF.

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“…Also the features are pure spectral features and have better retrieval performance due to multi-resolution analysis in both radial and circular directions of the shape. With an enhanced process, GFD can achieve retrieval performance on perspectively transformed shapes as high as it achieves on similarity transformed shapes 82 . Zhang and Lu have also shown that GFD outperforms contour shape descriptors such as CSS, FD and region-based shape descriptors such as Zernike moments, geometric moments and grid method 83 .…”

Section: (F) Generic Fourier Descriptormentioning

confidence: 99%

Global Contour and Region Based Shape Analysis and Similarity Measures

Chaudhuri

2013

DSJ

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Enhanced Generic Fourier Descriptors for object-based image retrieval

Cited by 41 publications

References 2 publications

Drums and Curve Descriptors

Drums and Curve Descriptors

Optimal Query-Based Relevance Feedback in Medical Image Retrieval Using Score Fusion-Based Classification

Global Contour and Region Based Shape Analysis and Similarity Measures

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