Local feature embedding for supervised image classification

Fisher discrimination dictionary sparse learning (FDDL) has led to interesting image recognition results where the Fisher discrimination criterion is subject to the coding coefficients. But Fisher discrimination criterion has the limitations of data distribution assumptions and does not consider the local manifold structure of the coding coefficients. In this paper, we will introduce a novel Fisher discrimination sparse learning based on graph embedding (GE-FDSL) scheme. First, we utilizes graph embedding framework to define intra-class compact matrix and inter-class separable matrix imposed on the coding coefficients of training samples to preserving the intra-class compactness and the inter-class separability for the training samples, which simultaneously consider the local manifold structure and label information of the coding coefficients. Then, a new Fisher discrimination criterion based on graph embedding is added to the object function of the sparse coding problem so that the coding coefficients have more discriminative power, where the dictionary atoms in the sparse coding model are associated with the class labels so that the reconstructed error is applied to classification. This method can learn a structured dictionary and sparse coefficients, and in the meantime, it will also keep the local manifold structure of the coding coefficients. So, they will be more discriminative. Experiments on many image databases show that the our algorithm has good classification and recognition performance.

Deep learning has been successfully applied in classification of white blood cells (WBCs), however, accuracy and processing time are found to be less than optimal hindering it from getting its full potential. This is due to imbalanced dataset, intra-class compactness, inter-class separability and overfitting problems. The main research idea is to enhance the classification and prediction accuracy of blood images while lowering processing time through the use of deep convolutional neural network (DCNN) architecture by using the modified loss function. The proposed system consists of a deep neural convolution network (DCNN) that will improve the classification accuracy by using modified loss function along with regularization. Firstly, images are pre-processed and fed through DCNN that contains different layers with different activation function for the feature extraction and classification. Along with modified loss function with regularization, weight function aids in the classification of WBCs by considering weights of samples belonging to each class for compensating the error arising due to imbalanced dataset. The processing time will be counted by each image to check the time enhancement. The classification accuracy and processing time are achieved using the dataset-master. Our proposed solution obtains better classification performance in the given dataset comparing with other previous methods. The proposed system enhanced the classification accuracy of 98.92% from 96.1% and a decrease in processing time from 0.354 to 0.216 s. Less time will be required by our proposed solution for achieving the model convergence with 9 epochs against the current convergence time of 13.5 epochs on average, epoch is the formation white blood cells (WBCs) and the development of granular cells. The proposed solution modified loss function to solve the adverse effect caused due to imbalance dataset by considering weight and use regularization technique for overfitting problem.

To achieve high scene classification performance of high spatial resolution remote sensing images (HSR-RSIs), it is important to learn a discriminative space in which the distance metric can precisely measure both similarity and dissimilarity of features and labels between images. While the traditional metric learning methods focus on preserving interclass separability, label consistency (LC) is less involved, and this might degrade scene images classification accuracy. Aiming at considering intraclass compactness in HSR-RSIs, we propose a discriminative distance metric learning method with LC (DDML-LC). The DDML-LC starts from the dense scale invariant feature transformation features extracted from HSR-RSIs, and then uses spatial pyramid maximum pooling with sparse coding to encode the features. In the learning process, the intraclass compactness and interclass separability are enforced while the global and local LC after the feature transformation is constrained, leading to a joint optimization of feature manifold, distance metric, and label distribution. The learned metric space can scale to discriminate out-of-sample HSR-RSIs that do not appear in the metric learning process. Experimental results on three data sets demonstrate the superior performance of the DDML-LC over state-of-the-art techniques in HSR-RSI classification.

In feature representation learning, robust features are expected to have intra-class compactness and inter-class separability. The traditional softmax loss concept ignores the intra-class compactness. Hence the discriminative power of deep features is weakened. This paper proposes a constrained center loss (CCL) to enable CNNs to extract robust features. Unlike the general center loss (CL) concept, class centers are analytically updated from the deep features in our formulation. In addition, we propose to use the entire training set to approximate class centers. By doing so, class centers can better capture the global information of feature space. To improve training efficiency, an alternative algorithm is proposed to optimize the joint supervision of softmax loss and CCL. Experiments are performed on four benchmark datasets. The results demonstrate that the proposed scheme outperforms several existing architectures.

Fine-grained visual categorization (FGVC) that aims to recognize objects from subcategories with very subtle differences remains a challenging task due to the large intra-class and small inter-class variation caused by, e.g., deformation, occlusion, illumination, background clutter, etc. A great deal of recent work tackles this problem by forcing the network to focus on partial discriminable features using attention mechanisms or part-based methods. However, these methods neglect the point that the network may learn to discriminate objects from identity-unrelated features, for instance, when backgrounds are discriminable in training samples, degrading the network’s generalization ability. In this paper, for the first time, we use disentangled representation learning to disentangle the fine-grained visual feature into two parts: the identity-related feature and the identity-unrelated feature. Only the identity-related feature is used for the final classification. Since identity-unrelated information is neglected in classification, intra-class variation is reduced while inter-class variation is amplified through the disentanglement, improving the classification performance as a result. Experimental results on three standard fine-grained visual categorization datasets, i.e., CUB-200-2011 (CUB), Stanford Cars (CAR) and FGVC-Aircraft (AIR), demonstrate the effectiveness of our method and show that we achieve state-of-the-art performance on the benchmarks.

Fruit classification has become increasingly important in a wide range of industrial and consumer-oriented applications. Automated fruit classification systems can significantly enhance efficiency by accurately identifying fruit varieties and supporting informed decisions. In this research, we propose a fast, accurate, and robust fruit classification approach leveraging Deep Learning (DL) techniques. The proposed approach is a fine-tuned, pretrained Visual Geometry Group-19 (VGG-19) convolutional neural network model, known for its depth and superior performance in image recognition and classification tasks. Unlike existing approaches, our proposed approach addresses the unique visual challenges of fruit images, including inter-class similarity, intra-class variability, occlusions, background clutter, and varying illumination conditions, which enhances generalization across both controlled and real-world datasets. This approach is rigorously evaluated using two distinct datasets obtained from Kaggle. Dataset 1 comprises high-resolution images captured under controlled conditions, while Dataset 2, derived from the Kaggle 360 Fruits dataset, contains diverse real-world images with varying backgrounds, lighting conditions, and occlusions. Experimental results demonstrate that the proposed model achieves exceptional classification accuracy, recording 99.65% on Dataset 1 and 97.98% on Dataset 2. The above findings underline the accuracy and stability of the fine-tuned VGG-19 model in processing clean and complicated images and pose a viable and scalable real-time fruit classification approach.

As an important and challenging problem in computer vision and graphics, keypoint-based object tracking is typically formulated in a spatio-temporal statistical learning framework. However, most existing keypoint trackers are incapable of effectively modeling and balancing the following three aspects in a simultaneous manner: temporal model coherence across frames, spatial model consistency within frames, and discriminative feature construction. To address this issue, we propose a robust keypoint tracker based on spatio-temporal multi-task structured output optimization driven by discriminative metric learning. Consequently, temporal model coherence is characterized by multi-task structured keypoint model learning over several adjacent frames, while spatial model consistency is modeled by solving a geometric verification based structured learning problem. Discriminative feature construction is enabled by metric learning to ensure the intra-class compactness and inter-class separability. Finally, the above three modules are simultaneously optimized in a joint learning scheme. Experimental results have demonstrated the effectiveness of our tracker.

In the fields of computer vision and graphics, keypoint-based object tracking is a fundamental and challenging problem, which is typically formulated in a spatio-temporal context modeling framework. However, many existing keypoint trackers are incapable of effectively modeling and balancing the following three aspects in a simultaneous manner: temporal model coherence across frames, spatial model consistency within frames, and discriminative feature construction. To address this problem, we propose a robust keypoint tracker based on spatio-temporal multi-task structured output optimization driven by discriminative metric learning. Consequently, temporal model coherence is characterized by multi-task structured keypoint model learning over several adjacent frames; spatial model consistency is modeled by solving a geometric verification based structured learning problem; discriminative feature construction is enabled by metric learning to ensure the intra-class compactness and inter-class separability. To achieve the goal of effective object tracking, we jointly optimize the above three modules in a spatio-temporal multi-task learning scheme. Furthermore, we incorporate this joint learning scheme into both single-object and multi-object tracking scenarios, resulting in robust tracking results. Experiments over several challenging datasets have justified the effectiveness of our single-object and multi-object trackers against the state-of-the-art.

Most popular image classification methods mainly focus on classification ability rather than recognizing new things. However, human lay emphasis on cognition first and then classification, which is closely related to human memory system. Though many memory models have been proposed, they are studied in word list whereas the reports about natural images are still limited. This paper proposes a memory model for image recognition and classification based on convolutional neural network and Bayesian decision. First the image feature is extracted by convolutional neural network and stored in binary form. Then the representation, storage and retrieval processes of visual images are modeled. The test image feature vector is matched in parallel to the studied image vectors, and the likelihood values are calculated. Finally, the odd that the test image belongs to a new class is computed based on all likelihood values. If the odd value is greater than a certain threshold, the test image is regarded as new; otherwise, the Bayesian decision rule for image classification is performed. Experimental results on Caltech-101 and Caltech-256 datasets show that the proposed method can perform well in image recognition and classification tasks. And the hit probability of the method is higher than two typical methods, SRC and ELM, at present while the false alarm rate is far lower than them.

Due to melanoma is one of the skin cancers with the highest mortality rate and have a large amount of data during the collection and diagnosis, there is an urgent need to improve the diagnostic efficiency and accuracy. However, there remain problems in analyzing medical big data for skin lesion application, such as the intra-class variation and inter-class similarity in skin lesion images and the lacks of ability to focus on the lesion area affecting the classification results of the model. To address these dilemmas, in this paper, we proposed a novel machine learning-based approach that builds on top of DenseNet. It combines the attention mechanism and large margin loss to enhance the classification accuracy in terms of intra-class compactness and inter-class separability. We evaluated our model on ISIC 2017 (International Skin Imaging Collaboration) dataset, which has achieved 92% of Mean AUC. The experimental results show the effectiveness of our solution outperforms the state-of-the-art significantly in classify skin lesion and can accurately classify malignant melanoma on medical images.

Convolutional neural networks (CNNs) have been applied to learn spatial features for high-resolution (HR) synthetic aperture radar (SAR) image classification. However, there has been little work on integrating the unique statistical distributions of SAR images which can reveal physical properties of terrain objects, into CNNs in a supervised feature learning framework. To address this problem, a novel end-to-end supervised classification method is proposed for HR SAR images by considering both spatial context and statistical features. First, to extract more effective spatial features from SAR images, a new deep spatial context encoder network (DSCEN) is proposed, which is a lightweight structure and can be effectively trained with a small number of samples. Meanwhile, to enhance the diversity of statistics, the nonstationary joint statistical model (NS-JSM) is adopted to form the global statistical features. Specifically, SAR images are transformed into the Gabor wavelet domain and the produced multi-subbands magnitudes and phases are modeled by the log-normal and uniform distribution. The covariance matrix is further utilized to capture the inter-scale and intra-scale nonstationary correlation between the statistical subbands and make the joint statistical features more compact and distinguishable. Considering complementary advantages, a feature fusion network (Fusion-Net) base on group compression and smooth normalization is constructed to embed the statistical features into the spatial features and optimize the fusion feature representation. As a result, our model can learn the discriminative features and improve the final classification performance. Experiments on four HR SAR images validate the superiority of the proposed method over other related algorithms.

Center-aligned domain adaptation network for image classification

Prototype learning has achieved good performance in many fields, showing higher flexibility and generalization. In this paper, we propose an efficient text line recognition method based on prototype learning with feature-level sliding windows for classification. In this framework, we combine weakly supervised discrimination and generation loss for learning feature representations with intra-class compactness and inter-class separability. Then, dynamic weighting and pseudo-label filtering are also adopted to reduce the influence of unreliable pseudo-labels and improve training stability significantly. Furthermore, we introduce consistency regularization to obtain more reliable confidence distributions and pseudo-labels. Experimental results on digital and Chinese handwritten text datasets demonstrate the superiority of our method and justify advantages in transfer learning on small-size datasets.KeywordsText recognitionPrototype learningConsistency regularizationConnectionist temporal classificationPseudo-label

Orthogonal neighborhood preserving discriminant analysis with patch embedding for face recognition

Automatic medical image segmentation plays a crucial role in many medical applications, such as disease diagnosis and treatment planning. Existing deep learning based models usually regarded the segmentation task as pixel-wise classification and neglected the semantic correlations of pixels across different images, leading to vague feature distribution. Moreover, pixel-wise annotated data is rare in medical domain, and the scarce annotated data usually exhibits the biased distribution against the desired one, hindering the performance improvement under the supervised learning setting. In this paper, we propose a novel Labeled-to-unlabeled Distribution Translation (L2uDT) framework with Semantic-oriented Contrastive Learning (SoCL), mainly for addressing the aforementioned issues in medical image segmentation. In SoCL, a semantic grouping module is designed to cluster pixels into a set of semantically coherent groups, and a semantic-oriented contrastive loss is advanced to constrain group-wise prototypes, so as to explicitly learn a feature space with intra-class compactness and inter-class separability. We then establish a L2uDT strategy to approximate the desired data distribution for unbiased optimization, where we translate the labeled data distribution with the guidance of extensive unlabeled data. In particular, a bias estimator is devised to measure the distribution bias, then a gradual-paced shift is derived to progressively translate the labeled data distribution to unlabeled one. Both labeled and translated data are leveraged to optimize the segmentation model simultaneously. We illustrate the effectiveness of the proposed method on two benchmark datasets, EndoScene and PROSTATEx, and our method achieves state-of-the-art performance, which clearly demonstrates its effectiveness for medical image segmentation. The source code is available at https://github.com/CityU-AIM-Group/L2uDT.