Support Vector Guided Dictionary Learning

Cai, Sijia; Zuo, Wangmeng; Zhang, Lei; Feng, Xiaoyi; Wang, Ping

doi:10.1007/978-3-319-10593-2_41

Cited by 96 publications

(71 citation statements)

References 29 publications

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“…We evaluate our approach on two face databases: the Extended YaleB database [38] and the AR face database [37], two handwritten digit datasets: MNIST [39] and USPS [40], and an object category dataset: Texture-25 [41]. We compare our method with SRC [12], M-SVM [17], FDDL [24], DKSVD [10], LCKSVD [16], SVGDL [42], S2D2 [19], JDL [11], OSSDL [22], SSR-D [36], and the recently proposed USSDL [18] and SSP-DL [21] algorithms. The last six methods (S2D2, JDL, OSSDL, SSR-D, USSDL, and SSP-DL) are semi-supervised dictionary learning models; the others are supervised dictionary learning methods.…”

Section: Experiments Resultsmentioning

confidence: 99%

Semi-supervised dictionary learning with label propagation for image classification

Chen

Yang

2017

Comp. Visual Media

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Sparse coding and supervised dictionary learning have rapidly developed in recent years, and achieved impressive performance in image classification. However, there is usually a limited number of labeled training samples and a huge amount of unlabeled data in practical image classification, which degrades the discrimination of the learned dictionary. How to effectively utilize unlabeled training data and explore the information hidden in unlabeled data has drawn much attention of researchers. In this paper, we propose a novel discriminative semisupervised dictionary learning method using label propagation (SSD-LP). Specifically, we utilize a label propagation algorithm based on class-specific reconstruction errors to accurately estimate the identities of unlabeled training samples, and develop an algorithm for optimizing the discriminative dictionary and discriminative coding vectors simultaneously. Extensive experiments on face recognition, digit recognition, and texture classification demonstrate the effectiveness of the proposed method.

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Section: Experiments Resultsmentioning

confidence: 99%

Semi-supervised dictionary learning with label propagation for image classification

Chen

Yang

2017

Comp. Visual Media

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“…As a benchmark for the results, different supervised and unsupervised (discriminative) dictionary learning techniques are used, namely (1) KSVD [7], (2) SRC [5] as unsupervised techniques and (3) LC-KSVD [8], (4) FDDL [15], (5) SVGDL [9] as supervised techniques. The results were compared by their performance for full label information for varying dictionary sizes.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, class specific sub-dictionaries are learned while maintaining discriminative coding vectors by applying the Fisher discrimination criterion. In the recent work of Cai et al [9], a new so called Support Vector Guided Dictionary Learning (SVGDL) algorithm is presented where the discrimination term consists of a weighted summation over squared distances between the pairs of coding vectors. The algorithm automatically assigns non-zero weights to critical vector pairs (the support vectors) leading to a generalized good performance in pattern recognition tasks.…”

Section: Related Workmentioning

confidence: 99%

“…This can be justified (as in [9]) because the goal is now to learn a discriminative dictionary and not to obtain sparse signals . However, once the dictionary is learned, a sparse representation can obtained by the orthogonal matching pursuit [16].…”

Section: Relative Attribute Guided Dictionary Learningmentioning

confidence: 99%

“…The coding vector specifying the linear combination is the sparse representation of the original input signal. Considering a set of n input signals Y ∈ R p×n , the goal is to find a dictionary D = [d 1 , d 2 , ..., d n ] ∈ R p×k and the sparse representation X ∈ R k×n such that Y ∼ = DX, where the term over-complete indicates k > n. Dictionaries can either be predefined as in the form of wavelets [6], or be learned from observations [7,8,9]. Also, many approaches have been developed to impose discriminative capabilities onto the dictionary learning process.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Relative attribute guided dictionary learning

Babaee¹,

Wolf²,

Rigoll³

2016

2016 IEEE International Conference on Image Processing (ICIP)

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A discriminative dictionary learning algorithm is proposed to find sparse signal representations using relative attributes as the available semantic information. In contrast, existing (discriminative) dictionary learning approaches utilize binary label information to enhance the discriminative property of the signal reconstruction residual, the sparse coding vectors or both. Compared to binary attributes or labels, relative attributes contain richer semantic information where the data is annotated with the attributes' strength. In this paper we use the relative attributes of training data indirectly to learn a discriminative dictionary. Precisely, we incorporate a rank function for the attributes in the dictionary learning process. In order to assess the quality of the obtained signals, we apply k-means clustering algorithm to the obtained signals and measure the clustering performance. Experimental results conducted on three datasets confirm that the proposed approach outperforms the state-of-the-art label based dictionary learning algorithms.

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Deep locality‐sensitive discriminative dictionary learning for semantic video analysis

2020

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Summary Video semantic analysis (VSA) has received significant attention in the area of Machine Learning for some time now, particularly video surveillance applications with sparse representation and dictionary learning. Studies have shown that the duo has significantly impacted on the classification performance of video detection analysis. In VSA, the locality structure of video semantic data containing more discriminative information is very essential for classification. However, there has been modest feat by the current SR‐based approaches to fully utilize the discriminative information for high performance. Furthermore, similar coding outcomes are missing from current video features with the same video category. To handle these issues, we first propose an improved deep learning algorithm—locality deep convolutional neural network algorithm (LDCNN) to better extract salient features and obtain local information from semantic video. Second, we propose a novel DL method, called deep locality‐sensitive discriminative dictionary learning (DLSDDL) for VSA. In the proposed DLSDDL, a discriminant loss function for the video category based on sparse coding of sparse coefficients is introduced into the structure of the locality‐sensitive dictionary learning (LSDL) method. After solving the optimized dictionary, the sparse coefficients for the testing video feature samples are obtained, and then the classification result for video semantic is realized by reducing the error existing between the original and recreated samples. The experiment results show that the proposed DLSDDL technique considerably increases the efficiency of video semantic detection as against competing methods used in our experiment.

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Support Vector Guided Dictionary Learning

Cited by 96 publications

References 29 publications

Semi-supervised dictionary learning with label propagation for image classification

Semi-supervised dictionary learning with label propagation for image classification

Relative attribute guided dictionary learning

Deep locality‐sensitive discriminative dictionary learning for semantic video analysis

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