Image retrieval using spatiograms of colors quantized by Gaussian Mixture Models

Zeng, Shan; Huang, Rui; Wang, Haibing; Kang, Zhen

doi:10.1016/j.neucom.2015.07.008

Cited by 97 publications

(72 citation statements)

References 27 publications

(54 reference statements)

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“…3 presents a comparison of MAP as a function of codebook size using different evaluation parameters. The proposed framework based on late fusion of FREAK and SIFT provides a better retrieval performance with higher values of precision and recall than the existing research [29], [34].…”

Section: Performance Evaluation Using Corel-1500mentioning

confidence: 85%

“…Dense SIFT is used for feature extraction and three different classifiers are applied to determine the best retrieval performance. Zeng et al [29] proposed an image representation that is based on generalized histogram of quantized colors. Gaussian Mixture Models and modification in CDH algorithm is proposed to make it more effective.…”

Section: Related Workmentioning

confidence: 99%

“…6 represents a sample of randomly selected images from each class of the Corel-1500. We evaluated the proposed framework on Corel-1500 and compared the results with state-of-the-art CBIR methods [29], [34]. Fig.…”

Section: Performance Evaluation Using Corel-1500mentioning

confidence: 99%

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Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors

Ali¹,

Mazhar²,

Iqbal³

et al. 2017

Preprint

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One of the challenges in Content-Based Image Retrieval (CBIR) is to reduce the semantic gaps between low-level features and high-level semantic concepts. In CBIR, the images are represented in the feature space and the performance of CBIR depends on the type of selected feature representation.Late fusion also known as visual words integration is applied to enhance the performance of image retrieval. The recent advances in image retrieval diverted the focus of research towards the use of binary descriptors as they are reported computationally efficient. In this paper, we aim to investigate the late fusion of Fast Retina Keypoint (FREAK) and Scale Invariant Feature Transform (SIFT). The late fusion of binary and local descriptor is selected because among binary descriptors, FREAK has shown good results in classification-based problems while SIFT is robust to translation, scaling, rotation and small distortions. The late fusion of FREAK and SIFT integrates the performance of both feature descriptors for an effective image retrieval. Experimental results and comparisons show that the proposed late fusion enhances the performances of image retrieval.

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Section: Performance Evaluation Using Corel-1500mentioning

confidence: 85%

Section: Related Workmentioning

confidence: 99%

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Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors

Ali¹,

Mazhar²,

Iqbal³

et al. 2017

Preprint

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“…Elalami [11] Poursistani et al [12] Guo et al [13] Subraet al [14] Walia et al [15] Irtaza et al [16] Elalami [17] Zeng et al [18] Proposed Method …”

Section: Precision-recallmentioning

confidence: 99%

Efficient Approach for Content Based Image Retrieval Using Multiple SVM in YACBIR

Laib¹,

Ait-Aoudia²

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…A few researches progress on image retrieval with respect to content similarity. So, a slight experimental prototype techniques have been conducted as QBIC [17], Photobook [18], Netra [19], Virage [20], SIMPLICITY [21] and Visual SEEK [22]. Furthermore, Content-based image retrieval (CBIR) is stated as all-inclusive surveys by [23,24].…”

Section: Introductionmentioning

confidence: 99%

Shape and Geometric Features-based Semantic Image Retrieval Using Multi-class Support Vector Machine

Malki

2017

Preprint

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Image retrieval using spatiograms of colors quantized by Gaussian Mixture Models

Cited by 97 publications

References 27 publications

Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors

Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors

Efficient Approach for Content Based Image Retrieval Using Multiple SVM in YACBIR

Shape and Geometric Features-based Semantic Image Retrieval Using Multi-class Support Vector Machine

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