A new image retrieval model based on monogenic signal representation

Zeng, Zhiyong; Song, Liwei; Zheng, Qicai; Chi, Yuan

doi:10.1016/j.jvcir.2015.08.014

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…GRMCB [72] and RLMIR [73] provide mAP as 76%. CDLIR [69], AMCI [75], IRMSR [76] and CRHOG [71] show mAP between 0.66 and 0.76. IKAMC [74] reports lowest mAP as 0.64.…”

Section: Results Of the Corel-1000 Dataset With Existing State-of-thementioning

confidence: 99%

“…To test the effectiveness and accuracy of the proposed method, the results of the Corel-1000 dataset were compared with the existing state-of-the-art methods. The existing methods include CDLIR [69], CBSSC [70], CRHOG [71], GRMCB [72], RLMIR [73], IKAMC [74], AMCI [75] and IRMSR [76]. A graphical representation of average precision of the proposed method as compared with existing state-of-the-art methods is shown in Figure 17.…”

Section: Results Of the Corel-1000 Dataset With Existing State-of-thementioning

confidence: 99%

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Deep Learning Using Symmetry, FAST Scores, Shape-Based Filtering and Spatial Mapping Integrated with CNN for Large Scale Image Retrieval

et al. 2020

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This article presents symmetry of sampling, scoring, scaling, filtering and suppression over deep convolutional neural networks in combination with a novel content-based image retrieval scheme to retrieve highly accurate results. For this, fusion of ResNet generated signatures is performed with the innovative image features. In the first step, symmetric sampling is performed on the images from the neighborhood key points. Thereafter, the rotated sampling patterns and pairwise comparisons are performed, which return image smoothing by applying standard deviation. These values of smoothed intensity are calculated as per local gradients. Box filtering adjusts the results of approximation of Gaussian with standard deviation to the lowest scale and suppressed by non-maximal technique. The resulting feature sets are scaled at various levels with parameterized smoothened images. The principal component analysis (PCA) reduced feature vectors are combined with the ResNet generated feature. Spatial color coordinates are integrated with convolutional neural network (CNN) extracted features to comprehensively represent the color channels. The proposed method is experimentally applied on challenging datasets including Cifar-100 (10), Cifar-10 (10), ALOT (250), Corel-10000 (10), Corel-1000 (10) and Fashion (15). The presented method shows remarkable results on texture datasets ALOT with 250 categories and fashion (15). The proposed method reports significant results on Cifar-10 and Cifar-100 benchmarks. Moreover, outstanding results are obtained for the Corel-1000 dataset in comparison with state-of-the-art methods.

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“…GRMCB [72] and RLMIR [73] provide mAP as 76%. CDLIR [69], AMCI [75], IRMSR [76] and CRHOG [71] show mAP between 0.66 and 0.76. IKAMC [74] reports lowest mAP as 0.64.…”

Section: Results Of the Corel-1000 Dataset With Existing State-of-thementioning

confidence: 99%