Learning an Order Preserving Image Similarity through Deep Ranking

Gupta, Nitin; Mujumdar, Shashank; Samanta, Suranjana; Mehta, Sameep

doi:10.1109/icpr.2018.8545492

Cited by 5 publications

(3 citation statements)

References 15 publications

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Order By: Relevance

“…The literature in [23] proposed a Two-channel network, in which the two-channel images (image pairs) are used as the CNN input, and the similarity learning was carried out by extracting the two-channel fusion features. In addition, to better describe image feature information for image similarity learning, variant network structure based on the convolutional neural network has been continuously applied, such as Sim-Net [24], NCC-net [25] and Deep Quadlet network [26].…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature Fusion for Enhancing Image Similarity Learning

Lü

Zhou

et al. 2019

IEEE Access

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Image similarity learning aims to exploit the correlation between different images by learning image appropriate common features. In recent years, the previous CNN-based methods have directly learned the similarity between image features, which effectively improves the learning efficiency of image similarity. However, it has the following limitations: (1) The extracted image features are too single to describe the content of the image accurately; and (2) the network training is limited by the amount of dataset size. Data augmentation and multi-feature fusion have been demonstrated to appropriately improve the model generalization ability for various vision tasks. This paper integrates these methods into the network structure to design three multi-feature fusion network. The input of network training adopts the same data from the multi-input method to realize data augmentation, and the diversity of extracted image features is greatly improved by fusing different features. Then, the trained network of different dataset size is utilized to verify the network training adaptability of multi-feature fusion network. Moreover, the influence of loss function and optimization algorithm on the learning efficiency of complicated networks have been studied. The experimental results show that our proposed method has excellent performance on the self-collected XPU and Totally-Looks-Like (TLL) dataset, the learning and model generalization ability of multi-feature fusion network are significantly improved through data augmentation and multi-feature fusion. The multi-feature fusion network proposed in this paper has strong adaptability to network training.

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Section: Introductionmentioning

confidence: 99%

Multi-Feature Fusion for Enhancing Image Similarity Learning

Lü

Zhou

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…the query I q than any negative candidate I n r in feature space, the triplet ranking loss [76,77,78,57,79] as in Eq. (3.6) is adopted for metric learning.…”

Section: Training Pipelinementioning

confidence: 99%

“…For accurate indexing, a positive reference image X p r is expected to be closer to the query X q than any negative reference X n r in the feature space. Thus, the triplet ranking loss [76,78,57,79] in Eq. (4.11) is used to train the image representation f θ , where [y] + = max(y, 0) ensures non-negative output and m is the empirical margin.…”

Section: Loss Functionmentioning

confidence: 99%