Multi-Label Dictionary Learning for Image Annotation

Jing, Xiao‐Yuan; Wu, Fei; Li, Zhiqiang; Hu, Ruimin; Zhang, David

doi:10.1109/tip.2016.2549459

Cited by 88 publications

(44 citation statements)

References 41 publications

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“…Lu et al [16] presented a more descriptive and robust visual bag-of-words (BOW) representation by semantic sparse recoding method for image annotation and classification. Jing et al [17] learned a label embedded dictionary as well as a sparse representation for image annotation. In addition, the sparse representation can be further enhanced by exploiting the kernel mapping, since it maps nonlinear separable features into a higher dimensional feature space, in which features with the similar labels are closely grouped together while those without the same labels become linearly separable.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

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Kernel‐Based Multiview Joint Sparse Coding for Image Annotation

Miao

Zhang

2017

Mathematical Problems in Engineering

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It remains a challenging task for automatic image annotation problem due to the semantic gap between visual features and semantic concepts. To reduce the gap, this paper puts forward a kernel-based multiview joint sparse coding (KMVJSC) framework for image annotation. In KMVJSC, different visual features as well as label information are considered as distinct views and are mapped to an implicit kernel space, in which the original nonlinear separable data become linearly separable. Then, all the views are integrated into a multiview joint sparse coding framework aiming to find a set of optimal sparse representations and discriminative dictionaries adaptively, which can effectively employ the complementary information of different views. An optimization algorithm is presented by extending K-singular value decomposition (KSVD) and accelerated proximal gradient (APG) algorithms to the kernel multiview framework. In addition, a label propagation scheme using the sparse reconstruction and weighted greedy label transfer algorithm is also proposed. Comparative experiments on three datasets have demonstrated the competitiveness of proposed approach compared with other related methods.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Following most research [10,13,17,19,32], all the test images are annotated by the top 5 relevant labels. To estimate the performance, we calculate precision ( ), recall ( ), and 1 -measure for each label.…”

Section: Experimental Settingsmentioning

confidence: 99%

Kernel‐Based Multiview Joint Sparse Coding for Image Annotation

Miao

Zhang

2017

Mathematical Problems in Engineering

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“…(2) Image classification methods embedded label consistency into sparse coding and dictionary learning methods and proposed a classification framework based on sparse coding automatic extraction. Jing et al [40] applied label consistency to image multilabel annotation tasks to achieve image classification. Zhang et al [41] proposed a valid implicit label consistency dictionary learning model to classify mechanical faults.…”

Section: Introductionmentioning

confidence: 99%

Image Classification Algorithm Based on Deep Learning-Kernel Function

Liu

2020

Scientific Programming

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Although the existing traditional image classification methods have been widely applied in practical problems, there are some problems in the application process, such as unsatisfactory effects, low classification accuracy, and weak adaptive ability. This method separates image feature extraction and classification into two steps for classification operation. The deep learning model has a powerful learning ability, which integrates the feature extraction and classification process into a whole to complete the image classification test, which can effectively improve the image classification accuracy. However, this method has the following problems in the application process: first, it is impossible to effectively approximate the complex functions in the deep learning model. Second, the deep learning model comes with a low classifier with low accuracy. So, this paper introduces the idea of sparse representation into the architecture of the deep learning network and comprehensively utilizes the sparse representation of well multidimensional data linear decomposition ability and the deep structural advantages of multilayer nonlinear mapping to complete the complex function approximation in the deep learning model. And a sparse representation classification method based on the optimized kernel function is proposed to replace the classifier in the deep learning model, thereby improving the image classification effect. Therefore, this paper proposes an image classification algorithm based on the stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation. The experimental results show that the proposed method not only has a higher average accuracy than other mainstream methods but also can be good adapted to various image databases. Compared with other deep learning methods, it can better solve the problems of complex function approximation and poor classifier effect, thus further improving image classification accuracy.

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“…These advances are mainly attributed to the existence of large-scale benchmarks such as ImageNet [10] and the deployment of a deep representation learning paradigm based on deep neural networks (DNNs) which learn the optimal representation and classifier jointly in an end-to-end fashion. As a closely related task, image annotation [11]- [15] aims to describe (rather than merely recognise) an image by annotating all visual concepts that appear in the image. This brings about a number of differences and new challenges compared to the image recognition problem.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation

Niu

Wen

et al. 2019

IEEE Trans. on Image Process.

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Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image annotation: 1) how to learn a rich feature representation suitable for predicting a diverse set of visual concepts ranging from object, scene to abstract concept; 2) how to annotate an image with the optimal number of class labels. To address the first issue, we propose a novel multi-scale deep model for extracting rich and discriminative features capable of representing a wide range of visual concepts. Specifically, a novel two-branch deep neural network architecture is proposed which comprises a very deep main network branch and a companion feature fusion network branch designed for fusing the multiscale features computed from the main branch. The deep model is also made multi-modal by taking noisy user-provided tags as model input to complement the image input. For tackling the second issue, we introduce a label quantity prediction auxiliary task to the main label prediction task to explicitly estimate the optimal label number for a given image. Extensive experiments are carried out on two large-scale image annotation benchmark datasets and the results show that our method significantly outperforms the state-of-the-art.Index Terms-Large-scale image annotation, multi-scale deep model, multi-modal deep model, label quantity prediction.

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Multi-Label Dictionary Learning for Image Annotation

Cited by 88 publications

References 41 publications

Kernel‐Based Multiview Joint Sparse Coding for Image Annotation

Kernel‐Based Multiview Joint Sparse Coding for Image Annotation

Image Classification Algorithm Based on Deep Learning-Kernel Function

Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation

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