Local Histogram Features Research Articles

Deep Learning and Histogram-Based Grain Size Analysis of Images.

Grain size analysis is used to study grain size and distribution. It is a critical indicator in sedimentary simulation experiments (SSEs), which aids in understanding hydrodynamic conditions and identifying the features of sedimentary environments. Existing methods for grain size analysis based on images primarily focus on scenarios where grain edges are distinct or grain arrangements are regular. However, these methods are not suitable for images from SSEs. We proposed a deep learning model incorporating histogram layers for the analysis of SSE images with fuzzy grain edges and irregular arrangements. Firstly, ResNet18 was used to extract features from SSE images. These features were then input into the histogram layer to obtain local histogram features, which were concatenated to form comprehensive histogram features for the entire image. Finally, the histogram features were connected to a fully connected layer to estimate the grain size corresponding to the cumulative volume percentage. In addition, an applied workflow was developed. The results demonstrate that the proposed method achieved higher accuracy than the eight other models and was highly consistent with manual results in practice. The proposed method enhances the efficiency and accuracy of grain size analysis for images with irregular grain distribution and improves the quantification and automation of grain size analysis in SSEs. It can also be applied for grain size analysis in fields such as soil and geotechnical engineering.

Enhanced fuzzy Gaussian networks for sputum image based Mycobacterium detection

ABSTRACT The primary step involved in the diagnosis of Mycobacterium tuberculosis is the Sputum Smear Microscopy. The limitations of manual detection can be avoided with an automated technique which is carried out in this study. This paper uses the Enhanced Fuzzy Gaussian Networks (EFGN) for the detection of TB, by integrating the Gaussian model with the fuzzy and the neural network. In EFGN, the classified output obtained from the enhanced fuzzy and neural network is combined together depending on the Gaussian mixture model. The sputum smear microscopic image acts as the input, to which the process of thresholding is imposed to get the segmented result. Local Gradient Pattern (LGP), length, density, area, and histogram features are utilized to classify and count the bacilli objects using EFGN classifier. The performance is estimated based on the factors, such as Segmentation Accuracy (SA), and Mean Squared Error (MSE).

Feature Generalization for Breast Cancer Detection in Histopathological Images.

Recent period has witnessed benchmarked performance of transfer learning using deep architectures in computer-aided diagnosis (CAD) of breast cancer. In this perspective, the pre-trained neural network needs to be fine-tuned with relevant data to extract useful features from the dataset. However, in addition to the computational overhead, it suffers the curse of overfitting in case of feature extraction from smaller datasets. Handcrafted feature extraction techniques as well as feature extraction using pre-trained deep networks come into rescue in aforementioned situation and have proved to be much more efficient and lightweight compared to deep architecture-based transfer learning techniques. This research has identified the competence of classifying breast cancer images using feature engineering and representation learning over the established and contemporary notion of using transfer learning techniques. Moreover, it has revealed superior feature learning capacity with feature fusion in contrast to the conventional belief of understanding unknown feature patterns better with representation learning alone. Experiments have been conducted on two different and popular breast cancer image datasets, namely, KIMIA Path960 and BreakHis datasets. A comparison of image-level accuracy is performed on these datasets using the above-mentioned feature extraction techniques. Image level accuracy of 97.81% is achieved for KIMIA Path960 dataset using individual features extracted with handcrafted (color histogram) technique. Fusion of uniform Local Binary Pattern (uLBP) and color histogram features has resulted in 99.17% of highest accuracy for the same dataset. Experimentation with BreakHis dataset has resulted in highest classification accuracy of 88.41% with color histogram features for images with 200X magnification factor. Finally, the results are contrasted to that of state-of-the-art and superior performances are observed on many occasions with the proposed fusion-based techniques. In case of BreakHis dataset, the highest accuracies 87.60% (with least standard deviation) and 85.77% are recorded for 200X and 400X magnification factors, respectively, and the results for the aforesaid magnification factors of images have exceeded the state-of-the-art.

An l ½ and Graph Regularized Subspace Clustering Method for Robust Image Segmentation

Segmenting meaningful visual structures from an image is a fundamental and most-addressed problem in image analysis algorithms. However, among factors such as diverse visual patterns, noise, complex backgrounds, and similar textures present in foreground and background, image segmentation still stands as a challenging research problem. In this article, the proposed method employs an unsupervised method that addresses image segmentation as subspace clustering of image feature vectors. Initially, an image is partitioned into a set of homogeneous regions called superpixels, from which Local Spectral Histogram features are computed. Subsequently, a feature data matrix is created whereupon subspace clustering methodology is applied. A single-stage optimization model is formulated with enhanced segmentation capabilities by the combined action of l ½ and l 2 norm minimization. Robustness of l ½ regularization toward both the noise and overestimation of sparsity provides simultaneous noise robustness and better subspace selection, respectively. While l 2 norm facilitates grouping effect. Hence, the designed optimization model ensures an improved sparse solution and a sparse representation matrix with an accurate block diagonal structure, which thereby favours getting properly segmented images. Then, experimental results of the proposed method are compared with the state-of-art algorithms. Results demonstrate the improved performance of our method over the state-of-art algorithms.

Classification of Cervical Precursor Lesions via Local Histogram and Cell Morphometric Features.

Cervical squamous intra-epithelial lesions (SIL) are precursor cancer lesions and their diagnosis is important because patients have a chance to be cured before cancer develops. In the diagnosis of the disease, pathologists decide by considering the cell distribution from the basal to the upper membrane. The idea, inspired by the pathologists' point of view, is based on the fact that cell amounts differ in the basal, central, and upper regions of tissue according to the level of Cervical Intraepithelial Neoplasia (CIN). Therefore, histogram information can be used for tissue classification so that the model can be explainable. In this study, two different classification schemes are proposed to show that the local histogram is a useful feature for the classification of cervical tissues. The first classifier is Kullback Leibler divergence-based, and the second one is the classification of the histogram by combining the embedding feature vector from morphometric features. These algorithms have been tested on a public dataset.The method we propose in the study achieved an accuracy performance of 78.69% in a data set where morphology-based methods were 69.07% and Convolutional Neural Network (CNN) patch-based algorithms were 75.77%. The proposed statistical features are robust for tackling real-life problems as they operate independently of the lesions manifold.

Skin Melanoma Detection in Microscopic Images Using HMM-Based Asymmetric Analysis and Expectation Maximization

Melanoma is one of the deadliest types of skin cancer with increasing incidence. The most definitive diagnosis method is the histopathological examination of the tissue sample. In this paper, a melanoma detection algorithm is proposed based on decision-level fusion and a Hidden Markov Model (HMM), whose parameters are optimized using Expectation Maximization (EM) and asymmetric analysis. The texture heterogeneity of the samples is determined using asymmetric analysis. A fusion-based HMM classifier trained using EM is introduced. For this purpose, a novel texture feature is extracted based on two local binary patterns, namely local difference pattern (LDP) and statistical histogram features of the microscopic image. Extensive experiments demonstrate that the proposed melanoma detection algorithm yields a total error of less than 0.04%.

Identification of multiple RNAs using feature fusion.

Detection of novel transcripts with deep sequencing has increased the demand for computational algorithms as their identification and validation using in vivo techniques is time-consuming, costly and unreliable. Most of these discovered transcripts belong to non-coding RNAs, a large group known for their diverse functional roles but lacks the common taxonomy. Thus, upon the identification of the absence of coding potential in them, it is crucial to recognize their prime functional category. To address this heterogeneity issue, we divide the ncRNAs into three classes and present RNA classifier (RNAC) that categorizes the RNAs into coding, housekeeping, small non-coding and long non-coding classes. RNAC utilizes the alignment-based genomic descriptors to extract statistical, local binary patterns and histogram features and fuse them to construct the classification models with extreme gradient boosting. The experiments are performed on four species, and the performance is assessed on multiclass and conventional binary classification (coding versus no-coding) problems. The proposed approach achieved >93% accuracy on both classification problems and also outperformed other well-known existing methods in coding potential prediction. This validates the usefulness of feature fusion for improved performance on both types of classification problems. Hence, RNAC is a valuable tool for the accurate identification of multiple RNAs .

LESH - feature extraction and cognitive machine learning techniques for recognition of lung cancer cells

The novel local energy-based shape histogram (LESH) feature mining strategy was proposed for different cancer predictions. This paper stretches out unique work to apply the LESH system to distinguish lung cancer using machine learning approaches. As the traditional neural network systems are complex and time consuming, machine learning approaches are considered in this work, which atomises the process of tumour identification. Before LESH feature extraction, we upgraded the radiograph pictures utilising a complexity constrained versatile histogram adjustment approach. Subjective machine learning classifiers are chosen specifically extraordinary learning machine approach; support vector machine (SVM) connected utilising the LESH impassive features for effective analysis of right therapeutic state in the X-ray and MRI pictures. The framework comprises of feature extraction stage, including choice stage and order stage. For including extraction/choice distinctive wavelets capacities have been connected to locate the noteworthy exactness. Grouping K-nearest neighbour calculation has been created/used for arrangement. The informational collection used in the proposed work has 114 knob regions and 73 non-knob districts. Precision levels of more than 96% for characterisation that have been accomplished which exhibit the benefits of the proposed approach.

A Cascaded Classification-Segmentation Reversible System for Computer-Aided Detection and Cells Counting in Microscopic Peripheral Blood Smear Basophils and Eosinophils Images

Computer-aided image analysis has a pivotal role in automated counting and classification of white blood cells (WBCs) in peripheral blood images. Due to their different characteristics, our proposed approach is based on investigating the variations between the basophils and eosinophils in terms of their color histogram, size, and shape before performing the segmentation process. Accordingly, we proposed a cascaded system using a classification-based segmentation process, called classification-segmentation reversible system (CSRS). Prior to applying the CSRS system, a Histogram-based Object to Background Disparity (HOBD) metric was deduced to determine the most appropriate color plane for performing the initial WBC detection (first segmentation). Investigating the local histogram features of both classes resulted in a 92.4% initial classification accuracy using the third-degree polynomial support vector machine (SVM) method. Subsequently, in the proposed CSRS approach, transformation-based segmentation algorithms were developed to fit the specific requirements of each of the two predicted classes. The proposed CSRS system is used, where the images from an initial classification process are fed into a second segmentation process for each class separately. The segmentation results demonstrated a similarity index of 94.9% for basophils, and 94.1% for eosinophils. Moreover, an average counting accuracy of 97.4% for both classes was achieved. In addition, a second classification was carried out after applying the CSRS, achieving a 5.2% increase in accuracy compared to the initial classification process.

Detecting Visual Objects by Edge Crawling

Abstract Content-based image retrieval methods develop rapidly with a growing scale of image repositories. They are usually based on comparing and indexing some image features. We developed a new algorithm for finding objects in images by traversing their edges. Moreover, we describe the objects by histograms of local features and angles. We use such a description to retrieve similar images fast. We performed extensive experiments on three established image datasets proving the effectiveness of the proposed method.

Robust Visual Tracking via a Collaborative Model Based on Locality-Constrained Sparse Coding

Target tracking is an important task in computer vision. Now many tracking algorithms have achieved great results. However, several challenges still hinder the development of tracking algorithms, such as abrupt motion, occlusion and so on. In order to use the feature information of the target more effectively and improve the accuracy and robustness of target tracking, a novel model is designed which is different from the previous discriminative component and generative component, and a novel discriminative-generative collaborative appearance model is presented to combine the two components in this paper. First, for the discriminative component, Locality-Constrained Sparse Coding Algorithm is proposed. In this algorithm, the objective function of the local feature of the target spatial information is determined by fusing the pyramid maximum pool and local feature histogram method. The objective function has three important parameters, which are solved by different optimization strategies. Second, for the generative component, the Histogram of Locality-Constrained Feature Algorithm is proposed. In this algorithm, the locality constraint is served to describe the spatial information of the target as a generative appearance model. Each image patch can be approximated by a linear combination of a local coordinate system formed by a dictionary whose elements are cluster centers that contain the most representative model of the target. Third, this paper designs a collaborative target tracking framework based on semi-supervised learning algorithm with locality constraint coding. The framework can quickly and robustly determine the feature information of the tracking region. The proposed algorithm is evaluated on the comprehensive test platform. The experimental results show that our method is more robust and efficient, and the precision and success rate of our algorithm are improved by 5.4% and 4.7%, respectively.

Local texture descriptors and projection histogram based handwritten Meitei Mayek character recognition

In this paper, we use the local texture descriptor and projection histogram feature for handwritten Meitei Mayek (Manipuri script) recognition. The different variations of the local binary pattern (LBP) namely, uniform LBP (ULBP), improved LBP (ILBP), center-symmetric LBP (CS-LBP) account for local texture descriptors. These features along with the projection histogram, separately and combined are presented to machine learning algorithms, k nearest neighbor (KNN), support vector machine (SVM) and Random Forest (RF) for classification of characters. The experiments by these feature descriptors with the classifiers have been evaluated on self-collected handwritten Meitei Mayek character dataset having 9800 samples. High accuracy is achieved even with the simple KNN classifier. Furthermore, classification with SVM and RF are explored, and the results are compared with the pixel-based methods which use the intensities value directly and a classic CNN model for recognition. The comparative results show that local texture descriptors and projection histogram strongly outperform pixel-based methods. The overall superior accuracy is achieved when the feature descriptors are combined with KNN classifier and performed even better than the CNN model.

Detecting Designated Building Areas From Remote Sensing Images Using Hierarchical Structural Constraints

Automatic detection of a designated building area (DBA) is a research hotspot in the field of target detection using remote sensing images. Target detection is urgently needed for tasks such as illegal building monitoring, dynamic land use monitoring, antiterrorism efforts, and military reconnaissance. The existing detection methods generally have low efficiency and poor detection accuracy due to the large size and complexity of remote sensing scenes. To address the problems of the current detection methods, this paper presents a DBA detection method that uses hierarchical structural constraints in remote sensing images. Our method was conducted in two main stages. (1) During keypoint generation, we proposed a screening method based on structural pattern descriptors. The local pattern feature of the initial keypoints was described by a multilevel local pattern histogram (MLPH) feature; then, we used one-class support vector machine (OC-SVM) merely to screen those building attribute keypoints. (2) To match the screened keypoints, we proposed a reliable DBA detection method based on matching the local structural similarities of the screened keypoints. We achieved precise keypoint matching by calculating the similarities of the local skeletal structures in the neighboring areas around the roughly matched keypoints to achieve DBA detection. We tested the proposed method on building area sets of different types and at different time phases. The experimental results show that the proposed method is both highly accurate and computationally efficient.

Improving the Bag-of-Words model with Spatial Pyramid matching using data augmentation for fine-grained arbitrary-oriented ship classification

ABSTRACTIn this letter, we investigate fine-grained classification of arbitrary-oriented ships in very high resolution optical imagery using Bag of Word model with Spatial Pyramid (SP-BoW). Given that based on ‘spatial pyramid’ of the histogram of local features, the final feature vectors not only count the multiplicity of ‘words’ but also represent their spatial topology. We attempt to improve the performance of this model by introducing augmented data for training phase. Our aim is to make the dataset big enough to be able to capture holistic variation of ship orientation. Three data augmentation operations are used including random rotate by an angle of modulo 90°, random flip-left-right, and random flip-top-bottom. Through this procedure, our trained SP-BoW model is able to get better generalization. The proposed approach is validated on the High-Resolution Ship Collections 2016 (HRSC2016) ship dataset. The results indicate that training on augmented data can significantly improve the performance of SP-BoW. Beside, compared to other state-of-the-art convolutional neural network-based approaches, the approach proposed in this research has yielded competitive results and could make it a good baseline for evaluating more sophisticated CNN architecture in the future.

Ear recognition via sparse coding of local features

An efficient scheme for human ear recognition is presented. This scheme comprises three main phases. First, the ear image is decomposed into a pyramid of progressively downgraded images, which allows the local patterns of the ear to be captured. Second, histograms of local features are extracted from each image in the pyramid and then concatenated to shape one single descriptor of the image. Third, the procedure is finalized by using decision making based on sparse coding. Experiments conducted on two datasets, composed of 125 and 221 subjects, respectively, have demonstrated the efficiency of the proposed strategy as compared to various existing methods. For instance, scores of 96.27% and 96.93% have been obtained for the datasets, respectively.

A performance evaluation of point pair features

More than a decade ago, the point pair features (PPFs) were introduced, showing a great potential for 3D object detection and pose estimation under very different conditions. Many modifications have been made to the original PPF, in each case showing varying degrees of improvement for specific datasets. However, to the best of our knowledge, no comprehensive evaluation of these features has been made. In this work, we evaluate PPFs on a large set of 3D scenes. We not only compare PPFs to local point cloud descriptors, but also investigate the internal variations of PPFs (different types of relations between two points). Our comparison is made on 7 publicly available datasets, showing variations on a number of parameters, e.g. acquisition technique, the number of objects/scenes and the amount of occlusion and clutter. We evaluate feature performance both at a point-wise object-scene correspondence level and for overall object detection and pose estimation in a RANSAC pipeline. Additionally, we also present object detection and pose estimation results for the original, voting based, PPF algorithm. Our results show that in general PPF is the top performer, however, there are datasets, which have low resolution data, where local histogram features show a higher performance than PPFs. We also found that PPFs compared to most local histogram features degrade faster under disturbances such as occlusion and clutter, however, PPFs still remain more descriptive on an absolute scale. The main contribution of this paper is a detailed analysis of PPFs, which highlights under which conditions PPFs perform particularly well as well as its main weaknesses.

Multi-gradient features and elongated quinary pattern encoding for image-based facial expression recognition

In this paper we propose a novel texture feature extraction method for posed and spontaneous image based facial expression recognition. The kernel Sobel filter is used with eight masks to derive the gradient components for each pixel in the image. Two types of gradient images are extracted for different directions denoted as xy and lr. The robust Elongated Quinary Pattern (EQP) descriptor is then used to quantize neighborhood local gradients around each point using five discrimination levels. We next divide each encoded image into a number of blocks and concatenate the local histogram features of each image individually. In order to boost the performance, we adopt a Multi Classifier System (MCS) to combine all scores of the encoded images based upon a multi-class Support Vector Machine (SVM) classifier. Experimental results show a significant improvement over previous approaches in the average recognition accuracy when using the spontaneous Moving Faces and People (MFP) database. In addition, the proposed method outperformed state-of-the-art methods when applied to the posed CK database with a recognition performance of 99.36% in the case of seven classes and 99.72% without the neutral class.

Histopathological Image Classification With Color Pattern Random Binary Hashing-Based PCANet and Matrix-Form Classifier.

The computer-aided diagnosis for histopathological images has attracted considerable attention. Principal component analysis network (PCANet) is a novel deep learning algorithm for feature learning with the simple network architecture and parameters. In this study, a color pattern random binary hashing-based PCANet (C-RBH-PCANet) algorithm is proposed to learn an effective feature representation from color histopathological images. The color norm pattern and angular pattern are extracted from the principal component images of R, G, and B color channels after cascaded PCA networks. The random binary encoding is then performed on both color norm pattern images and angular pattern images to generate multiple binary images. Moreover, we rearrange the pooled local histogram features by spatial pyramid pooling to a matrix-form for reducing the dimension of feature and preserving spatial information. Therefore, a C-RBH-PCANet and matrix-form classifier-based feature learning and classification framework is proposed for diagnosis of color histopathological images. The experimental results on three color histopathological image datasets show that the proposed C-RBH-PCANet algorithm is superior to the original PCANet and other conventional unsupervised deep learning algorithms, while the best performance is achieved by the proposed feature learning and classification framework that combines C-RBH-PCANet and matrix-form classifier.

ℓ1/2-norm regularized nonnegative low-rank and sparse affinity graph for remote sensing image segmentation

Segmentation of real-world remote sensing images is a challenge due to the complex texture information with high heterogeneity. Thus, graph-based image segmentation methods have been attracting great attention in the field of remote sensing. However, most of the traditional graph-based approaches fail to capture the intrinsic structure of the feature space and are sensitive to noises. A l1/2-norm regularization-based graph segmentation method is proposed to segment remote sensing images. First, we use the occlusion of the random texture model (ORTM) to extract the local histogram features. Then, a l1/2-norm regularized low-rank and sparse representation (L1/2NNLRS) is implemented to construct a l1/2-regularized nonnegative low-rank and sparse graph (L1/2NNLRS-graph), by the union of feature subspaces. Moreover, the L1/2NNLRS-graph has a high ability to discriminate the manifold intrinsic structure of highly homogeneous texture information. Meanwhile, the L1/2NNLRS representation takes advantage of the low-rank and sparse characteristics to remove the noises and corrupted data. Last, we introduce the L1/2NNLRS-graph into the graph regularization nonnegative matrix factorization to enhance the segmentation accuracy. The experimental results using remote sensing images show that when compared to five state-of-the-art image segmentation methods, the proposed method achieves more accurate segmentation results.

Unsupervised manifold alignment using soft-assign technique

In this paper, we propose a robust unsupervised algorithm for automatic alignment of two manifolds in different datasets with possibly different dimensionalities. The significant contribution is that the proposed alignment algorithm is performed automatically without any assumptions on the correspondences between the two manifolds. For such purpose, we first automatically extract local feature histograms at each point of the manifolds and establish an initial similarity between the two datasets by matching their histogram-based features. Based on such similarity, an embedding space is estimated where the distance between the two manifolds is minimized while maximally retaining the original structure of the manifolds. The elegance of this idea is that such complicated problem is formulated as a generalized eigenvalue problem, which can be easily solved. The alignment process is achieved by iteratively increasing the sparsity of correspondence matrix until the two manifolds are correctly aligned and consequently one can reveal their joint structure. We demonstrate the effectiveness of our algorithm on different datasets by aligning protein structures, 3D face models and facial images of different subjects under pose and lighting variations. Finally, we also compare with a state-of-the-art algorithm and the results show the superiority of the proposed manifold alignment in terms of vision effect and numerical accuracy.