Features Of Block Research Articles

Self-Training-Transductive-Learning Broad Learning System (STTL-BLS): A model for effective and efficient image classification

A novel model called Self-Training-Transductive-Learning Broad Learning System (STTL-BLS) is proposed for image classification. The model consists of two key blocks: Feature Block (FB) and Enhancement Block (EB). The FB utilizes the Proportion of Large Values Attention (PLVA) technique and an Encoder for feature extraction. Multiple FBs are cascaded in the model to learn discriminative features. The Enhancement Block (EB) enhances feature learning and prevents under-fitting on complex datasets. Additionally, an architecture that combines characteristics of Broad Learning System (BLS) and gradient descent is designed for STTL-BLS, enabling the model to leverage the advantages of both BLS and Convolutional Neural Networks (CNNs). Moreover, a training algorithm (STTL) that combines self-training and transductive learning is presented for the model to improve its generalization ability. Experimental results demonstrate that the accuracy of the proposed model surpasses all compared BLS variants and performs comparably or even superior to deep networks: on small-scale datasets, STTL-BLS has an average accuracy improvement of 14.82 percentage points compared to other models; on large-scale datasets, 12.95 percentage points. Notably, the proposed model exhibits low time complexity, particularly with the shortest testing time on the small-scale datasets among all compared models: it has an average testing time of 46.4 s less than other models. It proves to be an additional valuable solution for image classification tasks on both small- and large-scale datasets. The source code for this paper can be accessed at https://github.com/threedteam/sttl_bls.

Naming the Identified Feature Implementation Blocks from Software Source Code

Identifying software identifiers that implement a particular feature of a software product is known as feature identification. Feature identification is one of the most critical and popular processes performed by software engineers during software maintenance activity. However, a meaningful name must be assigned to the Identified Feature Implementation Block (IFIB) to complete the feature identification process. The feature naming process remains a challenging task, where the majority of existing approaches manually assign the name of the IFIB. In this paper, the approach called FeatureClouds was proposed, which can be exploited by software developers to name the IFIBs from software code. FeatureClouds approach incorporates word clouds visualization technique to name Feature Blocks (FBs) by using the most frequent words across these blocks. FeatureClouds had evaluated by assessing its added benefit to the current approaches in the literature, where limited tool support was supplied to software developers to distinguish feature names of the IFIBs. For validity, FeatureClouds had applied to draw shapes and ArgoUML software. The findings showed that the proposed approach achieved promising results according to well-known metrics in terms of Precision and Recall.

Digital image forensic using deep flower pollination with adaptive Harris hawk optimization

This paper presents a new segmentation algorithm and deep learning concept for innovative copy-move image forgery identification. In this work, the segmentation algorithm uses a new Adaptive Harris Hawk Optimization (AHHO) algorithm. The host image is segmented into irregular and non-overlapping blocks, named as Image Blocks (IB). Here the segmented image is compressed with a Discrete Cosine Transform (DCT). After that, the Zernike moments and Gabor filter-based features extraction techniques are processed to extract the Block Features (BF). At last, the tampered portions are classified with the hybrid Deep Neural Network (DNN) and Flower Pollination Algorithm (FPA) for forgery classification. This novel deep learning algorithm reduces the learning complexities and improves detection accuracy. The compression attacks are removed with anti-forensic blocking artefact removal concept. The experiments are conducted on the Benchmark dataset, CoMoFoD, and GRIP. For the benchmark dataset, precision, recall and F1 values are 91.27,100.0 and 96.07, respectively. For CoMoFoD dataset, the values of precision, recall and F1 are 92.57, 98.0 and 93.05, respectively. For GRIP dataset, the values of precision, recall and F1 are 97.02, 98.0 and 93.05, respectively. The implementation outcomes demonstrated that the proposed scheme is most effective in copy-move forgery recognition than the existing forgery detection approaches.

The cloud retrieval of combining sparse representation with subspace projection

Satellite cloud imagery can show the features and the evolution processes of all kinds of cloud systems from different aspects. Thus, adopting the content-based cloud image retrieval makes a big difference in supervising present weather conditions and studying the climate change. In order to optimize the combined features of the cloud picture and strengthen the generalization ability of its combined features, this paper presents an optimal method of combining the features of the sparse representation with the subspace projection. At first, we should extract its color, texture and shape, convert all the combined features, and divide them into different blocks. Then, we can make the sparse representation for each block's features, grouping them according to different atom variance and gaining both noticeable and unnoticeable features. Finally, we can count the power of the grouped features to get the subspace projection matrix, projecting the original combined features on it and achieving the optimal cloud picture features. The experiment turns out that the method of optimizing the cloud picture features in this paper is better than common descending dimension method and cloud retrieval technology in precision ratio and recall ratio. It indeed has a stronger optimization in the combined features as well as a lower time complexity in the process of the real-time retrieval, which indicates a brand new retrieval method.

An Effective Occipitomental View Enhancement Based on Adaptive Morphological Texture Analysis.

This paper aims to present an algorithm that specifically enhances maxillary sinuses using a novel contrast enhancement technique based on the adaptive morphological texture analysis for occipitomental view radiographs. First, the skull X-ray (SXR) is decomposed into rotational blocks (RBs). Second, each RB is rotated into various directions and processed using morphological kernels to obtain the dark and bright features. Third, a gradient-based block segmentation decomposes the interpolated feature maps into feature blocks (FBs). Finally, the histograms of FBs are equalized and overlaid locally to the input SXR. The performance of the proposed method was evaluated on an independent dataset, which comprises of 145 occipitomental view-based human SXR images. According to the experimental results, the proposed method is able to increase the diagnosis accuracy by 83.45% compared with the computed tomography modality as the gold standard.

Authentication and Recovery of Medical Diagnostic Image Using Dual Reversible Digital Watermarking

This paper proposes a new region-based tampering detection and recovering method that utilizes both reversible digital watermarking and quad-tree decomposition for medical diagnostic image's authentication. Firstly, the quad-tree decomposition is used to divide the original image into blocks with high homogeneity, and then we computer pixels' linear interpolation as each block's recovery feature. Secondly, these recovery features as the first layer watermarking information is embedded by using simple invertible integer transformation. In order to enhance the proposed method's security, the logistic chaotic map is exploited to choose each block's reference pixel. The second layer watermark comprises by the quad-tree information and essential parameters for extraction are embedded by LSB replacement. In the authentication phase, the embedded watermark is extracted and the source image is recovered, and the similar linear interpolation technique is utilized to get each block's feature. Therefore, the tampering detection and localization can be achieved through comparing the extracted feature with the recomputed one, and the extracted feature can be used to recover those tampered regions with high similarity to their original state. Experimental results show that, compared with previous similar existing scheme, the proposed method not only achieves high embedding capacity and good visual quality of marked and restored image, but also has more accuracy for tampering detection.

A Principal Component Clustering Approach to Object-Oriented Motion Segmentation and Estimation

This paper presents a framework for object-oriented scene segmentation in video, which uses motion as the major characteristic to distinguish different moving objects and then to segment the scene into object regions. From the feature block (FB) correspondences through at least two frames obtained via a tracking algorithm, the reference feature measurement matrix and feature displacement matrix are formed. We propose a technique for initial motion clustering of the FBs, in which the principal components (PC) of the two matrices are adopted as the motion features. The motion features have several advantages: (1) They are low-dimensional (2-dim). (2) They preserve well both the spatial closeness and the motion similarity of their corresponding FBs. (3) They tend to form distinctive clusters in the feature space, thus allowing simple clustering schemes to be applied. The Expectation-Maximization (EM) algorithm is applied for clustering the motion features. For those scenes involving mainly the camera motion, the PC-based motion features will exhibit nearly parallel lines in the feature space. This facilitates a simple and yet effective layer extraction scheme. The final motion-based segmentation involves labeling of all the blocks in the frame. The EM algorithm is again applied to minimize an energy function which takes motion consistency and neighborhood-sensitivity into account. The proposed algorithm has been applied to several test sequences and the simulation results suggest a promising potential for video applications.