Image Recognition by Using the Progressive Wavelet Correlation

Remote sensing includes all types of non-contact. surveys that are carried out from various measuring platforms. The tasks in this area are the following: inventory of agricultural land, control of the state of crops, forecasting yields. The aim of the work is to classify 6 types of crop images (wheat, rice, sugarcane, corn, cotton and jute) with greater accuracy. The paper considers an algorithm for primary processing and recognition of images of agricultural crops and algorithms for constructing a neural network for initial processing and recognition of images to solve problems of noise elimination, minimization, smoothing, normalization, segmentation and image recognition.

With the rapid development of urban rail transit in my country, the safety inspection of subway tunnel infrastructure is in urgent need of automated and intelligent theory and technology research and development. The subway tunnel lining environment is bad, there are a lot of noise and uneven illumination, and the texture features are complex. The traditional manual and image detection and recognition methods have gradually been replaced by more intelligent and effective deep learning algorithms. Therefore, this paper proposes a subway tunnel lining image recognition and detection algorithm that combines image processing and deep convolutional networks, which can quickly and accurately distinguish between cracked images and non-cracked images, and can realize intelligent target search and category labeling in complex images. In this experiment, a total of 4000 original images of subway tunnels were selected, and pixel-level shallow processing operations such as batch preprocessing, hierarchical feature analysis, and extraction of the circumscribed rectangle of the connected area were carried out. For the training sample library of crack images, pixel-level image processing experiments and classification and recognition experiments are carried out for the training sample library to obtain the optimal database processing results, and then the image recognition algorithm for complex cracks in subway tunnels is studied. Experiments show that there are more point noises and block noises in the results of the subway tunnel crack images, and the segmentation results of the crack images have relatively less noise residues. Due to the wall material, some cement materials are a little off, resulting in noise retention. This shows that the algorithm in this paper has a certain application value in the intelligent identification of actual subway tunnel diseases, and provides technical support and research ideas for the application of subsequent deep learning in subway tunnel disease detection.

In portable precision visual measurement, processing result of the measurement image has an important influence on final measurement accuracy in large field working space. However, the images taken in large field working space always have the following characteristics: containing a large amount of information, having a high redundancy and including a lot of background noises. In order to solve these problems, precision image processing and recognition algorithm is researched. Firstly, portable precision visual measurement method is introduced. Then, the design method of global control points including noncoding points and coding points is displayed. Finally, the image processing and recognition algorithm is investigated in detail, including the largest variance threshold method used to partly binary the image, the detection algorithm of global control points based on the constraint of distance and acreage and coding value recognition method through correcting the image feature. A large amount of experiments show that the image processing and recognition algorithm is accurate and effective to processing the image taken in the working space.

The paper is focused on the analysis of the relation between the stability of the flotation process and the efficiency of Machine Learning (ML) algorithms based on the flotation froth images. An ML process should enable researchers to construct Artificial Intelligence (AI) algorithms for flotation process control. The image of the flotation froth includes information characterizing the flotation process. The information can be extracted with the aid of the Image Recognition (IR) algorithms based on the ML. This enables construction of a flotation process control system in the mineral processing plant, which is based on the recognition of images of the flotation froth. The IR algorithms do not provide stable image recognition results and are not efficient in the situation where the parameters of the flotation process are highly unstable. The classification results were equal to 75.11% and 69.62% for a stable and unstable process, respectively. The experimental data collected at the Polish Pb/Zn mineral processing plant provided better insight to the relationships between the flotation process parameters and ML efficiency. These relationships were analyzed, and guidelines for the construction of the ML process for flotation process control have been formulated.

In order to get a good image recognition model, a lot of high-quality data must be trained. However, some special fields (such as battlefield, rare medical images, etc.) have the characteristics of difficult to obtain data and difficult to label. In order to get a small batch image recognition model with good performance under the condition of insufficient data, a Synchronous Deep Reinforcement Learning algorithm for small-batch image recognition is proposed, and the algorithm is used to recognize image data such as combat elements. The algorithm is based on reinforcement learning and convolutional neural network and so on, it is obtained by optimizing it. Through experimental analysis, the algorithm can quickly realize small batch image classification, and the accuracy rate is higher than other similar algorithms, which proves the practicability and feasibility of this algorithm.

Nowadays, the information processing capabilities and resource storage capabilities of computers have been greatly improved, which also provides support for the neural network technology. Convolutional neural networks have good characterization capabilities in computer vision tasks, such as image recognition technology. Aiming at the problem of high similarity image recognition and classification in a specific field, this paper proposes a high similarity image recognition and classification algorithm fused with convolutional neural networks. First, we extract the image texture features, train different types, and different resolution image sets and determine the optimal texture different parameter values. Second, we decompose the image into subimages according to the texture difference, extract the energy features of each subimage, and perform classification. Then, the input image feature vector is converted into a one-dimensional vector through the alternating 5-layer convolution and 3-layer pooling of convolutional neural networks. On this basis, different sizes of convolution kernels are used to extract different convolutions of the image features, and then use convolution to achieve the feature fusion of different dimensional convolutions. Finally, through the increase in the number of training and the increase in the amount of data, the network parameters are continuously optimized to improve the classification accuracy in the training set and in the test set. The actual accuracy of the weights is verified, and the convolutional neural network model with the highest classification accuracy is obtained. In the experiment, two image data sets of gems and apples are selected as the experimental data to classify and identify gems and determine the origin of apples. The experimental results show that the average identification accuracy of the algorithm is more than 90%.

Social networking sites like Facebook and Flickr allows users to upload billions of images, famous e commerce web sites such as flipkart, amazon etc. are also provides number of product related images to user. Images which are present in social multimedia networking websites are accompanied by different tags, comments, annotations and other related information which tend to form an image rich information networks. Due to existence of different related information to images (tag, annotation etc.) retrieval of images from image rich information networks is very challenging and complex task. In this paper we have proposed an integrated approach (S-Cbir) to retrieve an images from image rich information networks. The proposed algorithm consists integration of two different techniques such as link based image retrieval and content based image retrieval systems. K-Simrank algorithm is first applied to find out the node similarity in image rich information networks and then CBIR technique is applied to find out relevant images. SIFT, CEDD, feature and texture extraction descriptors are used in CBIR technique. The search results shows that proposed algorithm refines the search results and also improves the accuracy of search.

Digital image recognition based on Fractional-order-PCA-SVM coupling algorithm

The authors suggest a new technique for improvement of recognition reliability. The procedure is based on analysis of an image at several degrees of detail produced in the course of discrete wavelet transformation of the image. The results are presented by efficiency analysis of the algorithm for recognition of images by their distinctive points.

Traditional image recognition algorithms often face problems such as low recognition accuracy and insufficient robustness when facing complex scenes and multi-class image data. To this end, a hierarchical discriminant analysis (HDA)-based image recognition algorithm was proposed, which effectively improves image recognition performance by constructing a multi-scale feature extraction module, principal component analysis (PCA) dimensionality reduction, attention mechanism, and dynamic hierarchical adjustment strategy combined with a hierarchical feature extraction and discrimination model. The experiment was conducted on three public datasets: CIFAR-10, ImageNet subset (selecting 100 categories with a total of 150000 images, based on covering common object categories and moderate data volume for fair validation of algorithm performance), and MNIST. The performance was compared with models such as VGG16, ResNet50, SVM, KNN, Hierarchical CNN, EfficientNet, GoogLeNet, etc. The results indicated that the proposed method had higher recognition accuracy than other comparative algorithms on different datasets, with accuracies exceeding 90%. The proposed method performed better in terms of mean absolute error and root mean squared error. The F1 value curve of the proposed method was located at the top of the coordinate axis, reaching a maximum value of 92.39%, which was 14.56% higher than the lowest value of 78.24% in the EfficientNet model. This algorithm has better recognition accuracy than traditional algorithms on multiple public datasets, and has strong anti-interference ability and robustness, which can provide reference for optimizing the accuracy of image recognition.

Image recognition is a feature extraction and pattern matching technique to classify various objectives in computer science. In theory, it mainly relies on the images with distinguishable features as a good starting point. Each image has its own unique characteristics, and image features can be categorized into color features, texture features, and shape features etc. Therefore, using modern recognition techniques, we extract the image features through various algorithms to search for images with high similarities. There are many image recognition algorithms, and some of them are with high recognition rate while others are robust. But not all algorithms can be unconditionally implemented without adjusting to the real situation. In this paper, we introduce the classification algorithm based on Support Vector Machine (SVM) and the feature extraction method based on Principal Components Analysis (PCA). We employ the feature extraction algorithm to characterize facial features and recognize faces by comparing them to those stored in the training data set. Finally, we show the applications of feature extraction and classification algorithms in character recognition. The real characters printed on a cord are preprocessed by PCA, and then classified and identified by SVM with a good recognition rate if properly processed.

In the world of big data, large amounts of images are available in social media, corporate and even personal collections. A collection may grow quickly as new images are generated at high rates. The new images may cause changes in the distribution of existing classes or the emergence of new classes, resulting in the collection being dynamic and having concept drift. For efficient image retrieval from an image collection using a query, a hash table consisting of a set of hash functions is needed to transform images into binary hash codes which are used as the basis to find similar images to the query. If the image collection is dynamic, the hash table built at one time step may not work well at the next due to changes in the collection as a result of new images being added. Therefore, the hash table needs to be rebuilt or updated at successive time steps. Incremental hashing (ICH) is the first effective method to deal with the concept drift problem in image retrieval from dynamic collections. In ICH, a new hash table is learned based on newly emerging images only which represent data distribution of the current data environment. The new hash table is used to generate hash codes for all images including old and new ones. Due to the dynamic nature, new images of one class may not be similar to old images of the same class. In order to learn new hash table that preserves within-class similarity in both old and new images, incremental hashing with sample selection using dominant sets (ICHDS) is proposed in this paper, which selects representative samples from each class for training the new hash table. Experimental results show that ICHDS yields better retrieval performance than existing dynamic and static hashing methods.

We evaluate the small sample size (SSS) performance of evolutionary algorithms (EAs) for relevance feedback (RF) in image retrieval. We focus on the requirement to learn the user’s information need based on a small — between 2 and 25 — number of positive and negative training images. Despite this being a fundamental requirement, none of the existing works dealing with EAs for RF systematically evaluates their SSS performance. To address this issue, we compare four variants of EAs for RF. Common for all variants is the hierarchical, region-based image similarity model, with region and feature weights as parameters. The difference between the variants is in the objective function of the EA used to adjust the model parameters. The objective functions include: (O-1) precision; (O-2) average rank; (O-3) ratio of within-class (i.e., positive images) and between-class (i.e., positive and negative images) scatter; and (O-4) combination of O-2 and O-3. We note that — unlike O-1 and O-2 — O-3 and O-4 are not used in any of the existing works dealing with EAs for RF. The four variants are evaluated on five test databases, containing 61,895 general-purpose images, in 619 semantic categories. Results of the evaluation reveal that variants with objective functions O-3 and O-4 consistently outperform those with O-1 and O-2. Furthermore, comparison with the representative of the existing RF methods shows that EAs are both effective and efficient approaches for SSS learning in region-based image retrieval.

BackgroundPrevious research studies have demonstrated that medical content image retrieval can play an important role by assisting dermatologists in skin lesion diagnosis. However, current state-of-the-art approaches have not been adopted in routine consultation, partly due to the lack of interpretability limiting trust by clinical users.ObjectiveThis study developed a new image retrieval architecture for polarized or dermoscopic imaging guided by interpretable saliency maps. This approach provides better feature extraction, leading to better quantitative retrieval performance as well as providing interpretability for an eventual real-world implementation.MethodsContent-based image retrieval (CBIR) algorithms rely on the comparison of image features embedded by convolutional neural network (CNN) against a labeled data set. Saliency maps are computer vision–interpretable methods that highlight the most relevant regions for the prediction made by a neural network. By introducing a fine-tuning stage that includes saliency maps to guide feature extraction, the accuracy of image retrieval is optimized. We refer to this approach as saliency-enhanced CBIR (SE-CBIR). A reader study was designed at the University Hospital Zurich Dermatology Clinic to evaluate SE-CBIR’s retrieval accuracy as well as the impact of the participant’s confidence on the diagnosis.ResultsSE-CBIR improved the retrieval accuracy by 7% (77% vs 84%) when doing single-lesion retrieval against traditional CBIR. The reader study showed an overall increase in classification accuracy of 22% (62% vs 84%) when the participant is provided with SE-CBIR retrieved images. In addition, the overall confidence in the lesion’s diagnosis increased by 24%. Finally, the use of SE-CBIR as a support tool helped the participants reduce the number of nonmelanoma lesions previously diagnosed as melanoma (overdiagnosis) by 53%.ConclusionsSE-CBIR presents better retrieval accuracy compared to traditional CBIR CNN-based approaches. Furthermore, we have shown how these support tools can help dermatologists and residents improve diagnosis accuracy and confidence. Additionally, by introducing interpretable methods, we should expect increased acceptance and use of these tools in routine consultation.

Image recognition on streaming data is one of the most challenging topics in Image and Video Technology and incremental dimensionality reduction algorithms play a key role in online image recognition. In this paper, we present a novel supervised dimensionality reduction algorithm—Incremental Weighted Karhunen-Loève expansion based on the Between-class scatter matrix (IWKLB) for image recognition on streaming data. In comparison with Incremental PCA, IWKLB is more effective in terms of recognition rate. In comparison with Incremental LDA, it is free of small sample size problems and can directly be applied to high-dimensional image spaces with high efficiency. Experimental results conducted on AR, one benchmark face image database, demonstrate that IWKLB is more effective than IPCA and ILDA.