Remote Targets Recognition Based on Adaptive Weighting Feature Dictionaries and Joint Sparse Representations

Filtering of spam image-based email remains a major challenge for researchers. This paper presents a proposed work which is based on several facts such that spam images containing a large percentage of text which has characteristics or features different from other types of images. In addition to that, there is much similarity between the features of these images. These facts can be used to distinguish text regions spam images from others. A hybrid method based on combined features vector from text regions and features of the image is proposed. Two types of features are extracted. The first features extraction method is the local binary pattern (LBP) with extricating the image texture features directly, while the second is utilised to extricate features of image text regions only. The extracted features are used in individual and combination style in order to learn classifiers at the training stage. A one-class KNN classifier and two-class KNN classifier are applied separately. Each classifier was used in three fashion, with the text-regions features, with texture features in the image, and with merging both those features respectively. Experimental results showed that the appropriation of both image and text features together improves the effectiveness of the classification concerning the case in which only image or text features are used.

Sparse representation-based single image super-resolution (SISR) methods use a coupled overcomplete dictionary trained from high-resolution images/image patches. Since remote sensing (RS) satellites capture images of large areas, these images usually have poor spatial resolution and obtaining an effective dictionary as such would be very challenging. Moreover, traditional patch-based sparse representation models for reconstruction tend to give unstable sparse solution and produce visual artefact in the recovered images. To mitigate these problems, in this article, we have proposed an adaptive joint sparse representation-based SISR method that is dependent only on the input low-resolution image for dictionary training and sparse reconstruction. The new model combines patch-based local sparsity and group sparse representation-based nonlocal sparsity in a single framework, which helps in stabilizing the sparse solution and improve the SISR results. The experimental results are evaluated both visually and quantitatively for several RGB and multispectral RS datasets, where the proposed method shows improvements in peak signal-to-noise ratio by 1–4 dB and 2–3 dB over the state-of-the-art sparse representation- and deep learning-based SR methods, respectively. Land cover classification applied on the super-resolved images further validate the advantages of the proposed method. Finally, for practical RS applications, we have performed parallel implementation in general purpose graphics processing units and achieved significant speed ups (30–40×) in the execution time.

By means of a sparse collaborative representation mechanism, sparse-representation-based classifiers show a superior performance in hyperspectral image (HSI) classification. Exploiting the similarity and distinctiveness of HSI neighboring pixels, we propose a new nearest regularized joint sparse representation (NRJSR) classification method in this letter. In the classification process of the central test pixel, the weights of different neighboring pixels and the sparse representation coefficients of different training samples are optimized simultaneously within a regularized sparsity model, which can obtain adaptive weights with good joint sparse representation ability. An alternative iteration strategy is used to solve the regularized joint sparsity model. The proposed NRJSR algorithm is tested on two benchmark HSI data sets. Experimental results demonstrate that the proposed algorithm performs better than other sparsity-based algorithms and spectral and spectral–spatial support vector machine classifiers.

Early detection and accurate diagnosis for liver disease are very important. Due to the defects inherent in the ultrasound images and the complexity appearance of diseases, automatic classification for liver diseases in ultrasound images is a challenging task. In this paper, we introduce a novel method to classify focal liver lesions in ultrasound images. At first, we use an automatic image segmentation algorithm to delineate the lesion region. Then, according to the characteristics of liver lesions, we design a new image feature which is discriminative to liver lesions. Finally, six image features are processed by an improved sparse representation classifier to identify the diseases. We expand the sparse representation dictionary to optimize the classifier. Experimental results have shown that the proposed method could improve the classification accuracy in comparison with other state-of-the-art classifiers. It should be capable of assisting the physicians for liver disease diagnosis in the clinical practice.

With the tremendous growth of ICT (Information and Communication Technology), we are able to generate, store, share and transfer enormous amount of information. World Wide Web have further made is easy to access the information anytime, anywhere in the world. With the advent of high capacity communication links and storage devices even most of the information generated is of multimedia in nature. Images have major share in this information and the number of image achieves are growing with the jet speed Just having the tremendous amount of information is not useful unless we do not have the methodologies to effectively search the related data from it in minimum possible duration. The relativity of the image data is application specific. Here to search and retrieve the expected images from the database we need Content Based Image Retrieval (CBIR) system. CBIR extracts the features of query image and try to match them with the extracted features of images in the database. Then based on the similarity measures and threshold the best possible candidate matches are given as result. There have been many approaches to decide and extract the features of images in the database.

With the rapid development of information science and technology, image recognition technology plays an increasingly important role in the field of information security. However, in practical application, image recognition is easily affected by factors such as illumination, occlusion, background and other non-ideal conditions, so it is of great practical significance to seek robust image recognition technology. Sparse representation and collaborative representation can capture the essential features of face image, and obtain better recognition effect in image recognition. Therefore, this paper proposes an image recognition algorithm based on information fusion of sparsity and synergy. Experiments are carried out on the problems of collaborative representation classification and single sample image recognition. Experimental results show that, compared with sparse representation classification, collaborative representation classification achieves higher classification accuracy. When part of the pixel value image is occluded by 10%, the recognition rate of sparse representation algorithm is 99.1%, and the recognition rate is very good. Both algorithms have achieved very good recognition results in image recognition. Experiments show that sparse representation algorithm and collaborative representation algorithm improve the recognition rate of images.

Super-resolution guided knowledge distillation for low-resolution image classification

Joint representation and pattern learning for robust face recognition

When the gait feature is recognized, it will be hard to meet the real-time need due to the complexity and long calculated time of the recognition algorithm. Under this circumstance, a novel sparse representation based human gait recognition method is put forth which is based on. First of all, human silhouette is established and gait period is calculated. Next, we adopt Shifting Energy Image (SEI) as the feature of image and then extract Gabor Wavelet and Local Binary Pattern features. Finally, gait feature will be classified and recognized by using sparse representation. CASIA B gait database will be used in the experiment with a view of 90 degrees. The result witnesses that this method has higher recognition rate and can meet the needs of real-time.

This paper presents a new approach to single-image super-resolution, based on sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse representation for each patch of the low-resolution input, and then use the coefficients of this representation to generate the high-resolution output. Theoretical results from compressed sensing suggest that under mild conditions, the sparse representation can be correctly recovered from the downsampled signals. By jointly training two dictionaries for the low- and high-resolution image patches, we can enforce the similarity of sparse representations between the low resolution and high resolution image patch pair with respect to their own dictionaries. Therefore, the sparse representation of a low resolution image patch can be applied with the high resolution image patch dictionary to generate a high resolution image patch. The learned dictionary pair is a more compact representation of the patch pairs, compared to previous approaches, which simply sample a large amount of image patch pairs, reducing the computational cost substantially. The effectiveness of such a sparsity prior is demonstrated for both general image super-resolution and the special case of face hallucination. In both cases, our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods. In addition, the local sparse modeling of our approach is naturally robust to noise, and therefore the proposed algorithm can handle super-resolution with noisy inputs in a more unified framework.

Spectrum analysis can quickly extract and analyze frequency domain features of signal, and it has been widely applied in fields of image processing, noise processing and signal processing. Fast Fourier transform (FFT) is fast and efficient, because it can efficiently decrease complexity of discrete Fourier transform. As a consequence, FFT is a very good method for image frequency spectrum analysis. In this paper, we propose to consolidate frequency domain representation by sparse representation (SR) and collaborative representation classification (CRC) which has excellent performance in comparison with general sparse representation-associated classification algorithms. Our proposed novel method has three main phases. The first phase utilizes FFT to extract frequency domain features of original images, which are complementary with representations of the original images. The second phase of our proposed novel method exploits CRC or SR to obtain scores of original images and obtained features, respectively. The third phase integrates the scores of original images and obtained features and uses them to classify images. The major contribution of the proposed method is that it is usually more robust than methods using only FFT, CRC or state-of-art method CIRLRC for image classification. The experiments of image classification demonstrate that the simultaneous use of FFT and CRC or sparse representation classification has high accuracy on image recognition.

Single image super-resolution (SR) algorithms based on joint dictionaries and sparse representations of image patches have received significant attention in the literature and deliver the state-of-the-art results. Recently, Gaussian mixture models (GMMs) have emerged as favored prior for natural image patches in various image restoration problems. In this paper, we approach the single image SR problem by using a joint GMM learnt from concatenated vectors of high and low resolution patches sampled from a large database of pairs of high resolution and the corresponding low resolution images. Covariance matrices of the learnt Gaussian models capture the inherent correlations between high and low resolution patches, which are utilized for inferring high resolution patches from given low resolution patches. The proposed joint GMM method can be interpreted as the GMM analogue of joint dictionary-based algorithms for single image SR. We study the performance of the proposed joint GMM method by comparing with various competing algorithms for single image SR. Our experiments on various natural images demonstrate the competitive performance obtained by the proposed method at low computational cost.

In this paper we propose a technique for obtaining a high resolution (HR) image from a single low resolution (LR) image -using joint learning dictionary - on the basis of image statistic research. It suggests that with an appropriate choice of an over-complete dictionary, image patches can be well represented as a sparse linear combination. Medical imaging for clinical analysis and medical intervention is being used for creating visual representations of the interior of a body, as well as visual representation of the function of some organs or tissues (physiology). A number of medical imaging techniques are in use like MRI, CT scan, X-rays and Optical Coherence Tomography (OCT). OCT is one of the new technologies in medical imaging and one of its uses is in ophthalmology where it is being used for analysis of the choroidal thickness in the eyes in healthy and disease states such as age-related macular degeneration, central serous chorioretinopathy, diabetic retinopathy and inherited retinal dystrophies. We have proposed a technique for enhancing the OCT images which can be used for clearly identifying and analyzing the particular diseases. Our method uses dictionary learning technique for generating a high resolution image from a single input LR image. We train two joint dictionaries, one with OCT images and the second with multiple different natural images, and compare the results with previous SR technique. Proposed method for both dictionaries produces HR images which are comparatively superior in quality with the other proposed method of SR. Proposed technique is very effective for noisy OCT images and produces up-sampled and enhanced OCT images.

A new method classifying power quality disturbances (PQD) based on random matrix transform (RMT) and sparse representation classification (SRC) by L1-minimization is presented. First, the PQD signals are characterized by random matrix lower-dimensional projection based on compressive sensing theory. Then, every test sample from feature vectors is represented as a sparse linear combination of training samples. The PQD type assign to the object class that minimizes the residual between test sample and its sparse representation by solving L1-minimization problem. RMT feature extraction method is extremely efficient to generate and independent of the training dataset. Compared with support vector machine (SVM), the SRC algorithm needs neither training process nor combination of two-class classifiers for multiclass classification. Simulation results show that the proposed feature extraction and classification method has high classification correct ratio in strong noise condition.

In the field of computer vision and image processing CBIR (Content based image retrieval) system become an active research area. The CBIR technique involves feature extraction based on image features (like color, shape and texture) and defining rules for comparing image. A classifier is also used by authors for image classification. For this purpose an image database taken from James Z. Wang research group website is used by many authors. Due to enormous increase in the size of database traditional methods of image retrieval by using keywords does not provide accurate result. Thus the concept of CBIR system is emerged. In last couple of years many method of feature extraction is developed but since now researcher are not able to develop a system which has gained high accuracy of human visual perception in distinguishing pictures. Therefore earlier researchers have concentrated to increase the retrieval accuracy of CBIR system by using different combination of features. The objective of this paper is to present review on a CBIR technique which combined four kinds of features set including every kind of feature for developing feature vector. Color based features (histogram), statistical features (mean, median and standard deviation), spatial features (Gabor feature and wavelet features) and texture features (GLCM) is used for forming feature vector. A SVM classifier is used with CBIR system to increase retrieval accuracy. SVM (support vector machine) is a machine learning classifier which provides good classification accuracy. Keywords: CBIR, SVM, NN, GLCM, HSV Color space, Euclidean distance matrix.