On the use of high-order feature propagation in Graph Convolution Networks with Manifold Regularization

Graph Convolutional Networks (GCNs) have received a lot of attention in pattern recognition and machine learning. In this paper, we present a revisited scheme for the new method called "GCNs with Manifold Regularization" (GCNMR). While manifold regularization can add additional information, the GCN-based semi-supervised classification process cannot consider the full layer-wise structured information. Inspired by graph-based label propagation approaches, we will integrate high-order feature propagation into each GCN layer. High-order feature propagation over the graph can fully exploit the structured information provided by the latter at all the GCN's layers. It fully exploits the clustering assumption, which is valid for structured data but not well exploited in GCNs. Our proposed scheme would lead to more informative GCNs. Using the revisited model, we will conduct several semi-supervised classification experiments on public image datasets containing objects, faces and digits: Extended Yale, PF01, Caltech101 and MNIST. We will also consider three citation networks. The proposed scheme performs well compared to several semi-supervised methods. With respect to the recent GCNMR approach, the average improvements were 2.2%, 4.5%, 1.0% and 10.6% on Extended Yale, PF01, Caltech101 and MNIST, respectively.This work is supported in part by the University of the Basque Country UPV/EHU grant GIU19/027

Multi-View Graph Fusion for Semi-Supervised Learning: Application to Image-Based Face Beauty Prediction

Facial Beauty Prediction (FBP) is an important visual recognition problem to evaluate the attractiveness of faces according to human perception. Most existing FBP methods are based on supervised solutions using geometric or deep features. Semi-supervised learning for FBP is an almost unexplored research area. In this work, we propose a graph-based semi-supervised method in which multiple graphs are constructed to find the appropriate graph representation of the face images (with and without scores). The proposed method combines both geometric and deep feature-based graphs to produce a high-level representation of face images instead of using a single face descriptor and also improves the discriminative ability of graph-based score propagation methods. In addition to the data graph, our proposed approach fuses an additional graph adaptively built on the predicted beauty values. Experimental results on the SCUTFBP-5500 facial beauty dataset demonstrate the superiority of the proposed algorithm compared to other state-of-the-art methods

Ensemble learning via feature selection and multiple transformed subsets: Application to image classification

[EN]In the machine learning field, especially in classification tasks, the model's design and construction are very important. Constructing the model via a limited set of features may sometimes bound the classification performance and lead to non-optimal performances that some algorithms can provide. To this end, Ensemble learning methods were proposed in the literature. These methods' main goal is to learn a set of models that provide features or predictions whose joint use could lead to a performance better than that obtained by the single model. In this paper, we propose three variants of a new efficient ensemble learning approach that was able to enhance the classification performance of a linear discriminant embedding method. As a case study we consider the efficient "Inter-class sparsity discriminative least square regression" method. We seek the estimation of an enhanced data representation. Instead of deploying multiple classifiers on top of the transformed features, we target the estimation of multiple extracted feature subsets obtained by multiple learned linear embeddings. These are associated with subsets of ranked original features. Multiple feature subsets were used for estimating the transformations. The derived extracted feature subsets were concatenated to form a single data representation vector that is used in the classification process. Many factors were studied and investigated in this paper including (Parameter combinations, number of models, different training percentages, feature selection methods combinations, etc.). Our proposed approach has been benchmarked on different image datasets of various sizes and types (faces, objects and scenes). The proposed scheme achieved competitive performance on four face image datasets (Extended Yale B, LFW-a, Gorgia and FEI) as well as on the COIL20 object dataset and the Outdoor Scene dataset. We measured the performance of our proposed schemes in comparison to (the single model ICS_DLSR, RDA_GD, RSLDA, PCE, LDE, LDA, SVM as well as the KNN algorithm) The conducted experiments showed that the proposed approach can enhance the classification performance in an efficient manner compared to the single-model based learning and was able to outperform its competing methods

A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic Data

In this paper we present a practical approach for generating an occlusion-free textured 3D map of urban facades by the synergistic use of terrestrial images, 3D point clouds and area-based information. Particularly in dense urban environments, the high presence of urban objects in front of the facades causes significant difficulties for several stages in computational building modeling. Major challenges lie on the one hand in extracting complete 3D facade quadrilateral delimitations and on the other hand in generating occlusion-free facade textures. For these reasons, we describe a straightforward approach for completing and recovering facade geometry and textures by exploiting the data complementarity of terrestrial multi-source imagery and area-based information

Chasing a Better Decision Margin for Discriminative Histopathological Breast Cancer Image Classification

When considering a large dataset of histopathologic breast images captured at various magnification levels, the process of distinguishing between benign and malignant cancer from these images can be time-intensive. The automation of histopathological breast cancer image classification holds significant promise for expediting pathology diagnoses and reducing the analysis time. Convolutional neural networks (CNNs) have recently gained traction for their ability to more accurately classify histopathological breast cancer images. CNNs excel at extracting distinctive features that emphasize semantic information. However, traditional CNNs employing the softmax loss function often struggle to achieve the necessary discriminatory power for this task. To address this challenge, a set of angular margin-based softmax loss functions have emerged, including angular softmax (A-Softmax), large margin cosine loss (CosFace), and additive angular margin (ArcFace), each sharing a common objective: maximizing inter-class variation while minimizing intra-class variation. This study delves into these three loss functions and their potential to extract distinguishing features while expanding the decision boundary between classes. Rigorous experimentation on a well-established histopathological breast cancer image dataset, BreakHis, has been conducted. As per the results, it is evident that CosFace focuses on augmenting the differences between classes, while A-Softmax and ArcFace tend to emphasize augmenting within-class variations. These observations underscore the efficacy of margin penalties on angular softmax losses in enhancing feature discrimination within the embedding space. These loss functions consistently outperform softmax-based techniques, either by widening the gaps among classes or enhancing the compactness of individual classes.This work is partially supported by the project GUI19/027 and by the grant PID2021-126701OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”

Deep Learning with Discriminative Margin Loss for Cross-Domain Consumer-to-Shop Clothes Retrieval

Consumer-to-shop clothes retrieval refers to the problem of matching photos taken by customers with their counterparts in the shop. Due to some problems, such as a large number of clothing categories, different appearances of clothing items due to different camera angles and shooting conditions, different background environments, and different body postures, the retrieval accuracy of traditional consumer-to-shop models is always low. With advances in convolutional neural networks (CNNs), the accuracy of garment retrieval has been significantly improved. Most approaches addressing this problem use single CNNs in conjunction with a softmax loss function to extract discriminative features. In the fashion domain, negative pairs can have small or large visual differences that make it difficult to minimize intraclass variance and maximize interclass variance with softmax. Margin-based softmax losses such as Additive Margin-Softmax (aka CosFace) improve the discriminative power of the original softmax loss, but since they consider the same margin for the positive and negative pairs, they are not suitable for cross-domain fashion search. In this work, we introduce the cross-domain discriminative margin loss (DML) to deal with the large variability of negative pairs in fashion. DML learns two different margins for positive and negative pairs such that the negative margin is larger than the positive margin, which provides stronger intraclass reduction for negative pairs. The experiments conducted on publicly available fashion datasets DARN and two benchmarks of the DeepFashion dataset—(1) Consumer-to-Shop Clothes Retrieval and (2) InShop Clothes Retrieval—confirm that the proposed loss function not only outperforms the existing loss functions but also achieves the best performance

Blood Cell Revolution: Unveiling 11 Distinct Types with ‘Naturalize’ Augmentation

Artificial intelligence (AI) has emerged as a cutting-edge tool, simultaneously accelerating, securing, and enhancing the diagnosis and treatment of patients. An exemplification of this capability is evident in the analysis of peripheral blood smears (PBS). In university medical centers, hematologists routinely examine hundreds of PBS slides daily to validate or correct outcomes produced by advanced hematology analyzers assessing samples from potentially problematic patients. This process may logically lead to erroneous PBC readings, posing risks to patient health. AI functions as a transformative tool, significantly improving the accuracy and precision of readings and diagnoses. This study reshapes the parameters of blood cell classification, harnessing the capabilities of AI and broadening the scope from 5 to 11 specific blood cell categories with the challenging 11-class PBC dataset. This transformation facilitates a more profound exploration of blood cell diversity, surpassing prior constraints in medical image analysis. Our approach combines state-of-the-art deep learning techniques, including pre-trained ConvNets, ViTb16 models, and custom CNN architectures. We employ transfer learning, fine-tuning, and ensemble strategies, such as CBAM and Averaging ensembles, to achieve unprecedented accuracy and interpretability. Our fully fine-tuned EfficientNetV2 B0 model sets a new standard, with a macro-average precision, recall, and F1-score of 91%, 90%, and 90%, respectively, and an average accuracy of 93%. This breakthrough underscores the transformative potential of 11-class blood cell classification for more precise medical diagnoses. Moreover, our groundbreaking “Naturalize” augmentation technique produces remarkable results. The 2K-PBC dataset generated with “Naturalize” boasts a macro-average precision, recall, and F1-score of 97%, along with an average accuracy of 96% when leveraging the fully fine-tuned EfficientNetV2 B0 model. This innovation not only elevates classification performance but also addresses data scarcity and bias in medical deep learning. Our research marks a paradigm shift in blood cell classification, enabling more nuanced and insightful medical analyses. The “Naturalize” technique’s impact extends beyond blood cell classification, emphasizing the vital role of diverse and comprehensive datasets in advancing healthcare applications through deep learning.This work is supported by grant PID2021-126701OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, and by grant GIU19/027 funded by the University of the Basque Country UPV/EHU

Multiple-view flexible semi-supervised classification through consistent graph construction and label propagation

Graph construction plays an essential role in graph-based label propagation since graphs give some information on the structure of the data manifold. While most graph construction methods rely on predefined distance calculation, recent algorithms merge the task of label propagation and graph construction in a single process. Moreover, the use of several descriptors is proved to outperform a single descriptor in representing the relation between the nodes. In this article, we propose a Multiple-View Consistent Graph construction and Label propagation algorithm (MVCGL) that simultaneously constructs a consistent graph based on several descriptors and performs label propagation over unlabeled samples. Furthermore, it provides a mapping function from the feature space to the label space with which we estimate the label of unseen samples via a linear projection. The constructed graph does not rely on a predefined similarity function and exploits data and label smoothness. Experiments conducted on three face and one handwritten digit databases show that the proposed method can gain better performance compared to other graph construction and label propagation methods.This work was partially funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, Spain, Programa Estatal de I+D+i Orientada a los Retos de la Sociedad, RTI2018-101045-B- C21, and the University of the Basque Country, GIU19/02