Condition-CNN: A hierarchical multi-label fashion image classification model

Research on tourism resource demand offers critical decision support for the protection, development, and marketing of tourism resources. It also improves personalized experiences and satisfaction for tourists, thus fostering the advancement of the tourism industry into broader and higher realms. It also improves personalized experiences and satisfaction for tourists, thus fostering the advancement of the tourism industry into broader and higher realms. The objective of this study is to extract characteristics of 3A-level and above scenic spots nationwide, facilitating the integration and utilization of tourism resources in the country. The objective of this study is to extract characteristics of 3A-level and above scenic spots nationwide, facilitating the integration and utilization of tourism information. This facilitates the automation and efficiency of tourism resource classification and provides suggestions for improving services at these scenic areas. In response to the abundant resources of major scenic areas across the country, a new approach has been developed. In response to the abundant resources of major scenic areas across the country, this study introduces a hierarchical multi-label classification model for tourism resources. tourism resource classification theme system issued by the Ministry of Culture and Tourism, and leveraging FastText for pre-training on scenic area introductory texts, this research combines the advantages of the hierarchical multi-label classification model with the theme system of the Ministry of Culture and Tourism. introductory texts, this research combines the traditional LSTM model with an attention-based Transformer model. Additionally, a Graph-Convolutional Network (GCN) is used to classify tourism resources. Convolutional Network (GCN) is employed as a hierarchical structure-aware encoder to construct the MiLCT, a hierarchical multi-label classification model, enabling sophisticated multi-label classification to be applied to the text. model, enabling sophisticated multi-label classification of tourism scenic area resource data. Experimental comparisons demonstrate that, with increasing classification levels, the proposed model outperforms those lacking GCN and Transformer components in terms of micro-Precision, micro-Recision, micro-Recision, and micro-Recision. micro-Precision, micro-Recall, and micro-F1 scores, this indicates that the hierarchical structural information of the model can significantly enhance its performance. In comparison to the hierarchical multi-label correlation model, the proposed model demonstrates enhanced performance across the evaluated metrics, indicating its efficacy in integrating multimodal features and providing more comprehensive and accurate data characterization.

In this study, a hierarchical multi-label classification method called HFFNet-2d is proposed for the automatic classification of scanning electron microscope (SEM) images of metal failure. The method combines the advantages of convolutional neural networks (CNN) and Vision Transformers (ViT) to effectively realize hierarchical feature extraction and classification of SEM images of fracture morphologies, enabling accurate identification of metal failure mechanisms at different scales. The dataset of high-quality SEM images in this work is sourced from reputable materials science publications for its comprehensive coverage of various failure modes and its suitability for training and validating the hierarchical multi-label classification model. The HFFNet-2d model can achieve a high accuracy of 97.71% in the first-level classification and 92.62% in the second-level sub-category identification. This performance surpasses the human experts on the same task. To ensure that the model predictions are sufficiently reliable, a multi-level gradcam algorithm is also introduced for checking the regions of interest of the Hierarchical model at two levels and the comparisons are made with human experts. It is anticipated that the optimization and extension of HFFNet-2d are conducive in diverse material systems and application scenarios to accelerate the intelligent process of material development and failure analysis, ultimately supporting the design of reliable and high-performance engineering materials.

Denborazko serieen datu meatzaritza arloko problema ohikoenetako bat da, denborazko serieen gainbegiratutako sailkapena. Problema honen helburua da, klaseetan banatuta dauden serie multzo batetik abiatuz, sailkatu gabeko beste serie batzuen klasea aurresango duen eredu ahalik eta zehatzena eraikitzea. Problema klasiko honen hedapen gisa, kasu batzuetan, denborazko serieak denboran zehar jasotzen dira, eta ohikoa da iragarpenak ahalik eta lasterren egin nahi izatea, datuak jasotzeak dakartzan kostuak aurrezteko asmoarekin edo klaseak berandu iragartzeak ekarri ditzakeen ondorio kaltegarriak ekiditeko. Egoera honetan, denborazko serieen sailkapen goiztiarra izeneko problema agertzen da, zeinaren helburua den ahalik eta sailkatzaile zehatzena eta aldi berean iragarpen azkarrenak egingo dituena eraikitzea. Nahiko intuitiboa da pentsatzea, seriearen puntu gehiago ditugunean eskuragai, hari buruzko informazio gehiago dugula eta beraz, haren klaseari buruzko iragarpen zehatzagoak burutzea errazagoa dela. Alderantziz, seriearen klasea goiz aurresan nahi badugu, informazio gutxiago izango dugu eta beraz, zailagoa izango da klasea ondo aurresatea. Beraz, zehaztasuna eta azkartasuna bi helburu kontrajarriak dira. Lan honetan, denborazko serieen sailkapen goiztiarrari soluzioa emango dion metodo berritzaile bat proposatzen dugu, helburu anitzeko problema gisa planteaturik. Literaturan aurretik proposatutako helburu bakarreko optimizazio problema gisa eraikitako beste eredu batekin konparatu dugu gure eredua eta ikusi dugu gureak emaitza hobeak ematen dituela zenbait oinarrizko datu-basetan.

This paper proposes a hierarchical multi-label classification method based on SVM with composite kernel for solving gene function prediction. The hierarchical multi-label classification problem is resolved into a set of binary classification tasks. A composite kernel based SVM (ck-SVM) is introduced to deal with the binary classification tasks. In estimation procedure of ck-SVM, a supervised clustering with over-sampling strategy is introduced for solving imbalance dataset learning problem and improve classification performance. Experimental results on benchmark datasets demonstrate that the proposed method improves the classification performance efficiently.

In this paper, we present a hierarchical multi-label classification system for visual concepts detection and image annotation. Hierarchical multi-label classification (HMLC) is a variant of classification where an instance may belong to multiple classes at the same time and these classes/labels are organized in a hierarchy. The system is composed of two parts: feature extraction and classification/annotation. The feature extraction part provides global and local descriptions of the images. These descriptions are then used to learn a classifier and to annotate an image with the corresponding concepts. To this end, we use predictive clustering trees (PCTs), which are able to classify target concepts that are organized in a hierarchy. Our approach to HMLC exploits the annotation hierarchy by building a single predictive clustering tree that can simultaneously predict all of the labels used to annotate an image. Moreover, we constructed ensembles (random forests) of PCTs, to improve the predictive performance. We tested our system on the image database from the ImageCLEF@ICPR 2010 photo annotation task. The extensive experiments conducted on the benchmark database show that our system has very high predictive performance and can be easily scaled to large number of visual concepts and large amounts of data.

Hierarchical Classification is a very important classification task for arranging data in a hierarchical structure. Hierarchical arrangement of data is one of the best methods to achieve better understanding of complex data. In this paper, we propose the HMAC method to perform Hierarchical Multi-label Associative Classification. This method uses multiple and negative rules to predict class-set and to filter exceptional cases in order to improve both accuracy and explanatory ability of the resulting classifier. Redundant rule pruning method for negative rules and rule ranking method for hierarchical associative classification are developed. Moreover, we propose a rule evaluation measure, Sim, that tread-off between F-measure and Jaccard's coefficient to encode hierarchical structure meaning of actual and predicted node collections. We show that Sim is robust and simple rule evaluation measure. Experimental results show that HMAC improves accuracy and explanatory ability of hierarchical classifiers compared with various rule ranking and pruning techniques.

The classification problems described in the Machine Learning literature usually relate to the classification of data in which each example is associated to a class belonging to a finite set of classes, all at the same level. However, there are classification issues, of a hierarchical nature, where the classes can be either subclasses or super classes of other classes. In many hierarchical problems, one or more examples may be associated with more than one class simultaneously. These problems are known as hierarchical multi-label classification (HMC) problems. In this work, the ML-KNN algorithm was used to predict hierarchical multi-label problems, in order to determine the number of classes that can be assigned to an example. Through the experiments performed on 10 protein function databases and the statistical analysis of the results, it can be shown that the adaptations performed in the ML-KNN algorithm brought significant performance improvements based on the hierarchical precision and recall metrics Hierarchical.

Deep hiearchical multi-label classification applied to chest X-ray abnormality taxonomies.

This commentary addresses the modeling and final analytical path taken, as well as the terminology used, in the paper "Hierarchical diagnostic classification models: a family of models for estimating and testing attribute hierarchies" by Templin and Bradshaw (Psychometrika, doi: 10.1007/s11336-013-9362-0, 2013). It raises several issues concerning use of cognitive diagnostic models that either assume attribute hierarchies or assume a certain form of attribute interactions. The issues raised are illustrated with examples, and references are provided for further examination.

Variable coded hierarchical fuzzy classification model using DNA coding and evolutionary programming

Deep Neural Network (DNN) models are widely used for image classification. While they offer high performance in terms of accuracy, researchers are concerned about if these models inappropriately make inferences using features irrelevant to the target object in a given image. To address this concern, we propose a metamorphic testing approach that assesses if a given inference is made based on irrelevant features. Specifically, we propose two metamorphic relations (MRs) to detect such unreliable inferences. These relations expect (a) the classification results with different labels or the same labels but less certainty from models after corrupting the relevant features of images, and (b) the classification results with the same labels after corrupting irrelevant features. The inferences that violate the metamorphic relations are regarded as unreliable inferences. Our evaluation demonstrated that our approach can effectively identify unreliable inferences for single-label classification models with an average precision of 64.1% and 96.4% for the two MRs, respectively. As for multi-label classification models, the corresponding precision for MR-1 and MR-2 is 78.2% and 86.5%, respectively. Further, we conducted an empirical study to understand the problem of unreliable inferences in practice. Specifically, we applied our approach to 18 pre-trained single-label image classification models and 3 multi-label classification models, and then examined their inferences on the ImageNet and COCO datasets. We found that unreliable inferences are pervasive. Specifically, for each model, more than thousands of correct classifications are actually made using irrelevant features. Next, we investigated the effect of such pervasive unreliable inferences, and found that they can cause significant degradation of a model’s overall accuracy. After including these unreliable inferences from the test set, the model’s accuracy can be significantly changed. Therefore, we recommend that developers should pay more attention to these unreliable inferences during the model evaluations. We also explored the correlation between model accuracy and the size of unreliable inferences. We found the inferences of the input with smaller objects are easier to be unreliable. Lastly, we found that the current model training methodologies can guide the models to learn object-relevant features to certain extent, but may not necessarily prevent the model from making unreliable inferences. We encourage the community to propose more effective training methodologies to address this issue.

Hierarchical multi-label text classification (HMTC) has become rather challenging when it requires handling large sets of closely related categories. We present a novel software that provides a hybrid embedding-based text representation for HMTC, shortened as HE-HMTC. It made full use of categories’ structure and their labels semantics to enrich the text representation, and therefore attempted to improve the classification performance of the text. In addition, HE-HMTC facilitates improving the accuracy of HMTC tasks. Moreover, our HE-HMTC can easily be generalized in other hierarchical classification tasks and achieve superior performance.

We present a structured learning approach to semantic annotation of RGB-D images. Our method learns to reason about spatial relations of objects and fuses low-level class predictions to a consistent interpretation of a scene. Our model incorporates color, depth and 3D scene features, on which an energy function is learned to directly optimize object class prediction using the loss-based maximum-margin principle of structural support vector machines. We evaluate our approach on the NYU V2 dataset of indoor scenes, a challenging dataset covering a wide variety of scene layouts and object classes. We hard-code much less information about the scene layout into our model then previous approaches, and instead learn object relations directly from the data. We find that our conditional random field approach improves upon previous work, setting a new state-of-the-art for the dataset.

Delineating the cropping area of cocoa agroforests is a major challenge in quantifying the contribution of land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multispectral optical images is difficult due to the similarity of the spectral characteristics of their canopies. Moreover, the frequent cloud cover in the tropics greatly impedes optical sensors. This study evaluated the potential of multiseason Sentinel-1 C-band synthetic aperture radar (SAR) imagery to discriminate cocoa agroforests from transition forests in a heterogeneous landscape in central Cameroon. We used an ensemble classifier, Random Forest (RF), to average the SAR image texture features of a grey level co-occurrence matrix (GLCM) across seasons. We then compared the classification performance with results from RapidEye optical data. Moreover, we assessed the performance of GLCM texture feature extraction at four different grey levels of quantization: 32 bits, 8 bits, 6 bits, and 4 bits. The classification’s overall accuracy (OA) from texture-based maps outperformed that from an optical image. The highest OA (88.8%) was recorded at the 6 bits grey level. This quantization level, in comparison to the initial 32 bits in the SAR images, reduced the class prediction error by 2.9%. The texture-based classification achieved an acceptable accuracy and revealed that cocoa agroforests have considerably fragmented the remnant transition forest patches. The Shannon entropy (H) or uncertainty provided a reliable validation of the class predictions and enabled inferences about discriminating inherently heterogeneous vegetation categories.

Deep Neural Networks for Radiographic Risk Stratification of Gliomas