Category-specific Dictionaries Research Articles

Data-driven dictionary design–based sparse classification method for intelligent fault diagnosis of planet bearings

Planet bearings have remained as the challenging components for health monitoring and diagnostics in the planetary transmission systems of helicopters and wind turbines, due to their intricate kinematic mechanisms, strong modulations, and heavy interferences from gear vibrations. To address intelligent diagnostics of planet bearings, this article presents a data-driven dictionary design–based sparse classification (DDD-SC) approach. DDD-SC is free of detecting the weak frequency features and can achieve reliable fault recognition performances for planet bearings without establishing any explicit classifiers. In the first step, DDD-SC implements the data-driven dictionary design with an overlapping segmentation strategy, which leverages the self-similarity features of planet bearing data and constructs the category-specific dictionaries with strong representation power. In the second step, DDD-SC implements the sparsity-based intelligent diagnosis with the sparse representation–based classification criterion and differentiates various planet bearing health states based on minimal sparse reconstruction errors. The effectiveness and superiority of DDD-SC for intelligent planet bearing fault diagnosis have been demonstrated with an experimental planetary transmission system. The extensive diagnosis results show that DDD-SC can achieve the highest diagnosis accuracy, strongest anti-noise performance, and lowest computation costs in comparison with three classical sparse representation–based classification and two advanced deep learning methods.

Category Dictionary Guided Unsupervised Domain Adaptation for Object Detection

Unsupervised domain adaption (UDA) is a promising solution to enhance the generalization ability of a model from a source domain to a target domain without manually annotating labels for target data. Recent works in cross-domain object detection mostly resort to adversarial feature adaptation to match the marginal distributions of two domains. However, perfect feature alignment is hard to achieve and is likely to cause negative transfer due to the high complexity of object detection. In this paper, we propose a category dictionary guided (CDG) UDA model for cross-domain object detection, which learns category-specific dictionaries from the source domain to represent the candidate boxes in target domain. The representation residual can be used for not only pseudo label assignment but also quality (e.g., IoU) estimation of the candidate box. A residual weighted self-training paradigm is then developed to implicitly align source and target domains for detection model training. Compared with decision boundary based classifiers such as softmax, the proposed CDG scheme can select more informative and reliable pseudo-boxes. Experimental results on benchmark datasets show that the proposed CDG significantly exceeds the state-of-the-arts in cross-domain object detection.

Discriminative dictionary learning algorithm with pairwise local constraints for histopathological image classification.

Histopathological image contains rich pathological information that is valued for the aided diagnosis of many diseases such as cancer. An important issue in histopathological image classification is how to learn a high-quality discriminative dictionary due to diverse tissue pattern, a variety of texture, and different morphologies structure. In this paper, we propose a discriminative dictionary learning algorithm with pairwise local constraints (PLCDDL) for histopathological image classification. Inspired by the one-to-one mapping between dictionary atom and profile, we learn a pair of discriminative graph Laplacian matrices that are less sensitive to noise or outliers to capture the locality and discriminating information of data manifold by utilizing the local geometry information of category-specific dictionaries rather than input data. Furthermore, graph-based pairwise local constraints are designed and incorporated into the original dictionary learning model to effectively encode the locality consistency with intra-class samples and the locality inconsistency with inter-class samples. Specifically, we learn the discriminative localities for representations by jointly optimizing both the intra-class locality and inter-class locality, which can significantly improve the discriminability and robustness of dictionary. Extensive experiments on the challenging datasets verify that the proposed PLCDDL algorithm can achieve a better classification accuracy and powerful robustness compared with the state-of-the-art dictionary learning methods. Graphical abstract The proposed PLCDDL algorithm. 1) A pair of graph Laplacian matrices are first learned based on the class-specific dictionaries. 2) Graph-based pairwise local constraints are designed to transfer the locality for coding coefficients. 3) Class-specific dictionaries can be further updated.

A Category-Specific Dictionary Learning Method Tailored for Reconstruction-Based Feature Coding

Bag-of-Visual-Words (BoVW) is still a useful image classification model when there is not enough data to use Deep Learning. In BoVW model, the practice of reducing the reconstruction errors of local features can improve the classification accuracy owing to the decrease of information loss. Many reconstruction-based coding methods are proposed to learn a visual dictionary and encode local features via minimizing the reconstruction errors of local features with constraints. Besides this, the accuracy can also be improved by learning the category-specific dictionaries and then encoding features based on these dictionaries. By considering the two practices together, we propose a simple category-specific dictionary learning method tailored for reconstruction-based feature coding. Our method can be used as a universal one to improve the classification accuracies of many reconstruction-based coding methods, which is the highlight of our method. Concretely, a universal dictionary is learned by employing a reconstruction-based coding method and then refined for each category to obtain the category-specific dictionary of this category. When encoding a feature by a category-specific dictionary, the visual words for encoding it are decided in advance by the indices, which correspond to the non-zero elements of its coding vector obtained with the universal dictionary. The effectiveness of our method is validated by observing whether there is an accuracy improvement after applying our method. Our results on Scene-15, Caltech-101, and UIUC-Sports datasets show that the accuracies of four representative coding methods are improved by about 0.3% to 2.7%, which experimentally demonstrates the universality and effectiveness of our method.

Learning Category-Specific Sharable and Exemplary Visual Elements for Image Classification

This paper presents a novel method to determine the discriminative image representation via visual dictionary learning framework for image classification task. Visual dictionary learning has the capacity to represent input image using an over-complete element set. Sparsity restrains distractors and prevents over-fitting. The two main characteristics benefit the classification solution. However, one shortcoming of existing dictionary learning is that it neglects to exploit the potential correlations across visual elements, especially from the category-specific feature space. To address this problem, we first propose to learn multiple discriminative category-specific dictionaries (DCSD) from all categories. The DCSD can explore the visual elements from each category in terms of sharable property. For this reason, these learned category-specific visual elements encourage image features from the same class to have the similar feature representations. In addition, exemplary data reflect the main characteristic of whole dataset and can improve the performance of algorithm that employs them. Therefore, we further propose a representative pattern dictionary (RPD) model to discover the exemplary visual elements for promoting the discriminative capability of feature representation. These exemplary visual elements are essentially a subset of over-complete visual elements and can represent the whole sample data effectively. Finally, we design a novel strategy that integrates the merits of object proposals and deep features jointly to strengthen the semantic information of image-level feature. Experimental results on benchmark datasets demonstrate the effectiveness of our method, which is shown to be superior to the recently competing dictionary learning and deep learning based image classification approaches.

Event detection in soccer videos using unsupervised learning of Spatio-temporal features based on pooled spatial pyramid model

Most existing researches for semantic analysis of soccer videos benefit from special approaches to bridge the semantic gap between low-level features and high-level events using a hierarchical structure. In this paper, we propose a novel data-driven model for automatic recognition of important events in soccer broadcast videos based on the analysis of spatio-temporal local features of video frames. Our presented algorithm explores the local visual content of video frames by focusing on spatial and temporal learned features in a low-dimensional transformed sparse space. The proposed algorithm, without using mid-level futures, dynamically extracts the most informative semantic concepts/features and improves the generality of the system. The dictionary learning process plays an important role in sparse coding and sparse representation-based event classification. In this paper, we present a novel dictionary learning method, which calculates several category-specific dictionaries by training the detected shots of various view categories. In order to evaluate the feasibility and effectiveness of the proposed algorithm, an extensive experimental investigation is conducted for the analysis, detection, and classification of soccer events on a large collection of video data. Experimental results indicate that our approach outperforms the state-of-the-art methods and demonstrate the effectiveness of the proposed approach.

Linear discrimination dictionary learning for shape descriptors

The complexity and variation of 3D models have posed a lot of challenges in 3D shape retrieval area, for example, the invariant representation and retrieval of nonrigid and noisy 3D shapes. This paper proposed a supervised dictionary learning scheme called Linear Discrimination Dictionary Learning (LDDL) which can learn shape representations that are insensitive to 3D shape deformations in the same category and different for shapes from different categories in the meantime. Besides, it can extract the subtle differences between 3D shapes for fine-grained shapes. To be specific, in this paper, category-specific dictionaries are learnt to encode subtle visual differences of shapes among different categories, a shared dictionary is learnt to encode common patterns of shapes among all the categories; with the Linear Discriminant Analysis (LDA) constraint on the learnt descriptors, the new descriptors can have small within-class scatter and big between-class scatter. Our method is efficient in training and can obtain promising shape retrieval performance on representative shape benchmark datasets.

Scene-free multi-class weather classification on single images

Multi-class weather classification is a fundamental and significant technique which has many potential applications, such as video surveillance and intelligent transportation. However, it is a challenging task due to the diversity of weather and lack of discriminate feature. Most existing weather classification methods only consider two-class weather conditions such as sunny-rainy or sunny-cloudy weather. Moreover, they predominantly focus on a fixed scene such as popular tourism and traffic scenario. In this paper, we propose a novel method for scene-free multi-class weather classification from single images based on multiple category-specific dictionary learning and multiple kernel learning. To improve the discrimination of image representation and enhance the performance of multiple weather classification, our approach extracts multiple weather features and learns dictionaries based on these features. To select a good subset of features, we utilize multiple kernel learning algorithm to learn an optimal linear combination of feature kernels. In addition, to evaluate the proposed approach, we collect an outdoor image set that contains 20K images, called MWI (Multi-class Weather Image) set. Experimental results show the effectiveness of the proposed method.

Accumulated reconstruction error vector (AREV): a semantic representation for cross-media retrieval

Cross-media retrieval aims to automatically perform the content-based search procedure among various media types (e.g., image, video and text), in which media representation plays an important role for providing the heterogeneous similarity measure. In this work, a novel semantic representation of cross-media, called accumulated reconstruction error vector (AREV), is proposed, which includes category-specific dictionary learning, media sample reconstruction, and accumulative reconstruction error concatenation. Instead of directly learning the correlation relationship among heterogeneous items in the same semantic groups, the AREV projects individually their original feature descriptions into a shared semantic space, in which each component is semantic consistent for various media types due to the consistency in category information. Experiments on the commonly used datasets, i.e. Wikipedia dataset and NUS-Wide dataset, show the good performance in terms of effectiveness and efficiency.

Jointly Learning Visually Correlated Dictionaries for Large-Scale Visual Recognition Applications.

Learning discriminative dictionaries for image content representation plays a critical role in visual recognition. In this paper, we present a joint dictionary learning (JDL) algorithm which exploits the inter-category visual correlations to learn more discriminative dictionaries. Given a group of visually correlated categories, JDL simultaneously learns one common dictionary and multiple category-specific dictionaries to explicitly separate the shared visual atoms from the category-specific ones. The problem of JDL is formulated as a joint optimization with a discrimination promotion term according to the Fisher discrimination criterion. A visual tree method is developed to cluster a large number of categories into a set of disjoint groups, so that each of them contains a reasonable number of visually correlated categories. The process of image category clustering helps JDL to learn better dictionaries for classification by ensuring that the categories in the same group are of strong visual correlations. Also, it makes JDL to be computationally affordable in large-scale applications. Three classification schemes are adopted to make full use of the dictionaries learned by JDL for visual content representation in the task of image categorization. The effectiveness of the proposed algorithms has been evaluated using two image databases containing 17 and 1,000 categories, respectively.

Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization

This paper targets fine-grained image categorization by learning a category-specific dictionary for each category and a shared dictionary for all the categories. Such category-specific dictionaries encode subtle visual differences among different categories, while the shared dictionary encodes common visual patterns among all the categories. To this end, we impose incoherence constraints among the different dictionaries in the objective of feature coding. In addition, to make the learnt dictionary stable, we also impose the constraint that each dictionary should be self-incoherent. Our proposed dictionary learning formulation not only applies to fine-grained classification, but also improves conventional basic-level object categorization and other tasks such as event recognition. Experimental results on five data sets show that our method can outperform the state-of-the-art fine-grained image categorization frameworks as well as sparse coding based dictionary learning frameworks. All these results demonstrate the effectiveness of our method.