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BCS glossary of computing and ICT
The BCS Glossary is the most authoritative and comprehensive glossary of its kind on the market today. This unrivalled study aid and reference tool has newly updated entries and is divided into themed sections, making it more than just a list of definitions. Written in a style that is easily accessible to anybody with an interest in computing, it is specifically designed to support those taking computer courses or courses where computers are used, in schools and Further Education colleges.
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Multilingual dictionary of IT security : English-German-French-Spanish-Italian
No detailed description available for \"Multilingual Dictionary of IT Security\".
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Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification
The employed dictionary plays an important role in sparse representation or sparse coding based image reconstruction and classification, while learning dictionaries from the training data has led to state-of-the-art results in image classification tasks. However, many dictionary learning models exploit only the discriminative information in either the representation coefficients or the representation residual, which limits their performance. In this paper we present a novel dictionary learning method based on the Fisher discrimination criterion. A structured dictionary, whose atoms have correspondences to the subject class labels, is learned, with which not only the representation residual can be used to distinguish different classes, but also the representation coefficients have small within-class scatter and big between-class scatter. The classification scheme associated with the proposed Fisher discrimination dictionary learning (FDDL) model is consequently presented by exploiting the discriminative information in both the representation residual and the representation coefficients. The proposed FDDL model is extensively evaluated on various image datasets, and it shows superior performance to many state-of-the-art dictionary learning methods in a variety of classification tasks.
Journal Article
Hydra: competing convolutional kernels for fast and accurate time series classification
We demonstrate a simple connection between dictionary methods for time series classification, which involve extracting and counting symbolic patterns in time series, and methods based on transforming input time series using convolutional kernels, namely Rocket and its variants. We show that by adjusting a single hyperparameter it is possible to move by degrees between models resembling dictionary methods and models resembling Rocket. We present Hydra, a simple, fast, and accurate dictionary method for time series classification using competing convolutional kernels, combining key aspects of both Rocket and conventional dictionary methods. Hydra is faster and more accurate than the most accurate existing dictionary methods, achieving similar accuracy to several of the most accurate current methods for time series classification. Hydra can also be combined with Rocket and its variants to significantly improve the accuracy of these methods.
Journal Article
Twin Contrastive Learning for Online Clustering
This paper proposes to perform online clustering by conducting twin contrastive learning (TCL) at the instance and cluster level. Specifically, we find that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the instance and cluster representation, respectively. Based on the observation, for a given dataset, the proposed TCL first constructs positive and negative pairs through data augmentations. Thereafter, in the row and column space of the feature matrix, instance- and cluster-level contrastive learning are respectively conducted by pulling together positive pairs while pushing apart the negatives. To alleviate the influence of intrinsic false-negative pairs and rectify cluster assignments, we adopt a confidence-based criterion to select pseudo-labels for boosting both the instance- and cluster-level contrastive learning. As a result, the clustering performance is further improved. Besides the elegant idea of twin contrastive learning, another advantage of TCL is that it could independently predict the cluster assignment for each instance, thus effortlessly fitting online scenarios. Extensive experiments on six widely-used image and text benchmarks demonstrate the effectiveness of TCL. The code is released on https://pengxi.me.
Journal Article
SUN Database: Exploring a Large Collection of Scene Categories
Progress in scene understanding requires reasoning about the rich and diverse visual environments that make up our daily experience. To this end, we propose the Scene Understanding database, a nearly exhaustive collection of scenes categorized at the same level of specificity as human discourse. The database contains 908 distinct scene categories and 131,072 images. Given this data with both scene and object labels available, we perform in-depth analysis of co-occurrence statistics and the contextual relationship. To better understand this large scale taxonomy of scene categories, we perform two human experiments: we quantify human scene recognition accuracy, and we measure how typical each image is of its assigned scene category. Next, we perform computational experiments: scene recognition with global image features, indoor versus outdoor classification, and “scene detection,” in which we relax the assumption that one image depicts only one scene category. Finally, we relate human experiments to machine performance and explore the relationship between human and machine recognition errors and the relationship between image “typicality” and machine recognition accuracy.
Journal Article
Single image shadow detection and removal based on feature fusion and multiple dictionary learning
In recent years, the analysis of natural image has made great progress while the image of the intrinsic component analysis can solve many computer vision problems, such as the image shadow detection and removal. This paper presents the novel model, which integrates the feature fusion and the multiple dictionary learning. Traditional model can hardly handle the challenge of reserving the removal accuracy while keeping the low time consuming. Inspire by the compressive sensing theory, traditional single dictionary scenario is extended to the multiple condition. The human visual system is more sensitive to the high frequency part of the image, and the high frequency part expresses most of the semantic information of the image. At the same time, the high frequency characteristic of the high and low resolution image is adopted in the dictionary training, which can effectively recover the loss in the high resolution image with high frequency information. This paper presents the integration of compressive sensing model with feature extraction to construct the two-stage methodology. Therefore, the feature fusion algorithm is applied to the dictionary training procedure to finalize the robust model. Simulation results proves the effectiveness of the model, which outperforms compared with the other state-of-the-art algorithms.
Journal Article