A Survey on Multi-label Classification for Images

Devkar, Radhika; Shiravale, Sankirti

doi:10.5120/ijca2017913398

Cited by 7 publications

(2 citation statements)

References 22 publications

(26 reference statements)

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“…Multi-label network classification is a well-known task that is being used in a wide variety of web-based and nonwebbased domains [12] which has achieved great progress due to the development of deep convolutional networks [13]. Although how DCNN best copes with multi-label images still remains an open problem, it has been successfully applied into many real-world applications [14]. CNN-RNN framework was proposed which combined recurrent neural networks (RNNs) and CNNs to learn a joint image-label embedding to characterize the semantic label dependency as well as the image-label relevance.…”

Section: Related Workmentioning

confidence: 99%

GraftNet: An Eﬃcient and Flexible Multi-label Image Classiﬁcation and Its Application

2021

Proceedings of 2021 the 11th International Workshop on Computer Science and Engineering

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A multi-label network with an adaptive hierarchical structure is presented in this paper. The proposed network is efficient and flexible; we call it GraftNet in abbreviation. The network is in a tree-like fashion which has a trunk and several branches. The trunk is pre-trained with a dynamic graph for generic feature extraction and branches separately trained on sub-datasets with single label to improve accuracy and efficiency. We employ efficient neural architecture search (ENAS) for the branches part which outperforms manually designed architectures on image classification. The proposed network could avoid the inherent problem of "catastrophic forgetting" problem in deep neural networks and the addition of new image classes to a network could be easier than retraining the whole network again with all the classes. The employment of ENAS helps our network to achieve high performance. The proposed hierarchical network is also suitable for imbalanced dataset. Compared against fine-tuning a deep network, the proposed network achieves significant reduction of training and testing effort. Experimental results show that it has good performance on our human attributes recognition task.

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Section: Related Workmentioning

confidence: 99%

GraftNet: An Eﬃcient and Flexible Multi-label Image Classiﬁcation and Its Application

2021

Proceedings of 2021 the 11th International Workshop on Computer Science and Engineering

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“…Though in the past multi-label classification was mainly motivated by the tasks of text categorization and medical diagnosis, it is increasingly important in modern applications, ranging from image classification (Boutell et al , 2004; Devkar and Shiravale, 2017; Shen et al , 2004; Zhang et al , 2014), music categorization (Li and Ogihara, 2003), protein function classification (Kolesov et al , 2014; Luo and Zincir-Heywood, 2005), and so forth. The naïve method is to treat a multi-label problem as M separate binary classification problems.…”

Section: Introductionmentioning

confidence: 99%

ML²S-SVM: multi-label least-squares support vector machine classifiers

2019

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Purpose Image classification is becoming a supporting technology in several image-processing tasks. Due to rich semantic information contained in the images, it is very popular for an image to have several labels or tags. This paper aims to develop a novel multi-label classification approach with superior performance. Design/methodology/approach Many multi-label classification problems share two main characteristics: label correlations and label imbalance. However, most of current methods are devoted to either model label relationship or to only deal with unbalanced problem with traditional single-label methods. In this paper, multi-label classification problem is regarded as an unbalanced multi-task learning problem. Multi-task least-squares support vector machine (MTLS-SVM) is generalized for this problem, renamed as multi-label LS-SVM (ML2S-SVM). Findings Experimental results on the emotions, scene, yeast and bibtex data sets indicate that the ML2S-SVM is competitive with respect to the state-of-the-art methods in terms of Hamming loss and instance-based F1 score. The values of resulting parameters largely influence the performance of ML2S-SVM, so it is necessary for users to identify proper parameters in advance. Originality/value On the basis of MTLS-SVM, a novel multi-label classification approach, ML2S-SVM, is put forward. This method can overcome the unbalanced problem but also explicitly models arbitrary order correlations among labels by allowing multiple labels to share a subspace. In addition, the multi-label classification approach has a wider range of applications. That is to say, it is not limited to the field of image classification.

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