Features For Retrieval Research Articles

The holistic forgetting of events and the (sometimes) fragmented forgetting of objects

Episodic events are typically retrieved and forgotten holistically. If you recall one element (e.g., a person), you are more likely to recall other elements from the same event (e.g., the location), a pattern that is retained over time in the presence of forgetting. In contrast, representations of individual items, such as objects, may be less coherently bound, such that object features are forgotten at different rates and retrieval dependency decreases across delay. To test the theoretical prediction that forgetting qualitatively differs across levels in a representational hierarchy, we investigated the potential dissociation between event and item memory across five experiments. Participants encoded three-element events comprising images of famous people, locations, and objects. We measured retrieval accuracy and the dependency between the retrieval of event associations and object features, immediately after encoding and after various delays (5 h to 3 days). Across experiments, retrieval accuracy decreased for both events and objects over time, revealing forgetting. Retrieval dependency for event elements (i.e., people, locations, and objects) did not change over time, suggesting the holistic forgetting of events. Retrieval dependency for object features (i.e., state and colour) was more variable. Depending on encoding and delay conditions across the experiments, we observed both fragmentation and holistic forgetting of object features. Our results suggest that event representations remain coherent over time, whereas object representations can, but do not always, fragment. This provides support for our representational hierarchy framework of forgetting, however there are (still to be determined) boundary conditions in relation to the fragmentation of object representations.

Neural Correlates of Retrieval Success and Precision: A Functional Magnetic Resonance Imaging Study.

Prior studies examining the neural mechanisms underlying retrieval success and precision have yielded inconsistent results. Here, the neural correlates of success and precision were examined with a memory task that assessed precision for spatial location. A sample of healthy young adults underwent functional magnetic resonance imaging scanning during a single study-test cycle. At study, participants viewed a series of object images, each placed at a randomly selected location on an imaginary circle. At test, studied images were intermixed with new images and presented to the participants. The requirement was to move a cursor to the location of the studied image, guessing if necessary. Participants then signaled whether the presented image had been studied. Memory precision was quantified as the angular difference between the studied location and the location selected by the participant. A precision effect was evident in the left angular gyrus, where BOLD activity covaried with location accuracy. In addition, multivoxel pattern analysis revealed a significant item-level reinstatement effect in the angular gyrus for high-precision trials. There was no evidence of a retrieval success effect in this region. BOLD activity in the hippocampus was insensitive to both success and precision. These findings are partially consistent with prior evidence that success and precision are dissociable features of memory retrieval.

Application of Multimedia Information Retrieval Technology in Japanese Text Content Information Query Platform

The research and development of Japanese operating platform has become a new industry. At the same time, it is necessary to strengthen the research on query and feedback technology in MIR(Multimedia Information Retrieval). Based on this, this paper introduces the research status and progress of query and feedback technology in multimedia retrieval, and introduces the overall framework and technical links of MIR. This paper discusses the application of MIR technology in Japanese text content information query platform. A new multimodal retrieval mechanism is proposed, which combines cross-reference index and underlying features in retrieval and related feedback to realize unified processing of multimodal data. The time consumption of the algorithm is lower than that of the two comparative algorithms, which also verifies the superiority. It is feasible to apply the MIR technology proposed in this paper to the Japanese text content information query platform. It provides a new path for Japanese text content information retrieval.

ChatGeoAI: Enabling Geospatial Analysis for Public through Natural Language, with Large Language Models

Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to bridge the gap between natural language queries and executable code for geospatial analyses within the PyQGIS environment. It empowers non-expert users to leverage GIS technology without requiring deep knowledge of geospatial programming or tools. Through cutting-edge Natural Language Processing (NLP) techniques, including tailored entity recognition and ontology mapping, the framework accurately interprets user intents and translates them into specific GIS operations. Integration of geospatial ontologies enriches semantic comprehension, ensuring precise alignment between user descriptions, geospatial datasets, and geospatial analysis tasks. A code generation module empowered by Llama 2 converts these interpretations into PyQGIS scripts, enabling the execution of geospatial analysis and results visualization. Rigorous testing across a spectrum of geospatial analysis tasks, with incremental complexity, evaluates the framework and the performance of such a system, with LLM at its core. The proposed system demonstrates proficiency in handling various geometries, spatial relationships, and attribute queries, enabling accurate and efficient analysis of spatial datasets. Moreover, it offers robust error-handling mechanisms and supports tasks related to map styling, visualization, and data manipulation. However, it has some limitations, such as occasional struggles with ambiguous attribute names and aliases, which leads to potential inaccuracies in the filtering and retrieval of features. Despite these limitations, the system presents a promising solution for applications integrating LLMs into GIS and offers a flexible and user-friendly approach to geospatial analysis.

Analyzing Stellar Spectra for PRV by Accurate Modeling and Retrieval of Telluric Absorption Features

Ground-based Precision Radial Velocity (PRV) measurements are inevitably impeded by contamination from telluric absorption features, particularly in the infrared region. Thus, it is crucial to improve modeling of the telluric absorption features down to the spectral noise level. As part of the efforts towards improved PRV measurements, we have taken an existing atmospheric trace gas retrieval algorithm (GFIT) and have successfully adapted it to fit the telluric absorption features in stellar spectra down to the spectral noise level (typically ∼1%). We have established a stellar spectral fitting processing pipeline, Stellar-GFIT, to analyze a series of stellar spectra observed by two spectrographs, PARVI (1.1–1.76 μm) commissioned at the Palomar Observatory (Palomar Mountain, CA) and iSHELL (1–5 μm) deployed on the IRTF (Mauna Kea, HI). For this, we have (1) implemented a Gaussian instrumental line shape function, (2) generated atmospheric models (consisting of temperature, pressure, and volume mixing ratios of all the known trace gases) for the particular observation sites and times, (3) employed the most up-to-date spectroscopic parameters in the target spectral regions, and finally (4) developed a series of spectral fitting intervals of ∼60 cm−1 width, i.e., micro-windows, customized to the individual orders of each spectrograph. Stellar-GFIT is also capable of handling non-telluric features, such as transitions from a gas cell placed in the starlight beam and stellar features if a model spectrum template is available for the target star. We present spectrum fits from the observations of various target stars and discuss the performance and advantages of our novel approach. One of the major strengths of Stellar-GFIT is an ability to adjust the abundance of atmospheric trace gases simultaneously with determining the stellar doppler shift, mitigating any adverse impacts of short-timescale variations of water vapor.

Application of the Semi-Supervised Learning Approach for Pavement Defect Detection.

Road surface quality is essential for driver comfort and safety, making it crucial to monitor pavement conditions and detect defects in real time. However, the diversity of defects and the complexity of ambient conditions make it challenging to develop an effective and robust classification and detection algorithm. In this study, we adopted a semi-supervised learning approach to train ResNet-18 for image feature retrieval and then classification and detection of pavement defects. The resulting feature embedding vectors from image patches were retrieved, concatenated, and randomly sampled to model a multivariate normal distribution based on the only one-class training pavement image dataset. The calibration pavement image dataset was used to determine the defect score threshold based on the receiver operating characteristic curve, with the Mahalanobis distance employed as a metric to evaluate differences between normal and defect pavement images. Finally, a heatmap derived from the defect score map for the testing dataset was overlaid on the original pavement images to provide insight into the network's decisions and guide measures to improve its performance. The results demonstrate that the model's classification accuracy improved from 0.868 to 0.887 using the expanded and augmented pavement image data based on the analysis of heatmaps.

Progressively global–local fusion with explicit guidance for accurate and robust 3d hand pose reconstruction

Parametric and non-parametric methods are two commonly used strategies in current 3D hand pose reconstruction. Parametric methods predict low-dimensional parameters to fit a predefined hand model to the input image. Benefiting from the prior knowledge of hand models, parametric methods guarantee plausible hand poses, whereas the pose estimation accuracy is limited due to nonlinear regression and spatial information loss. Differently, non-parametric methods directly estimate the coordinates of keypoints or mesh vertices from the input image. The reconstructed 3D hand poses show high precision but may be less robust. In this paper, we integrate the advantages of two methods for accurate and robust hand pose reconstruction. Specifically, we disentangle the hand pose reconstruction into global modeling and local refinement, which is performed in a coarse-to-fine manner. Firstly, we utilize global features from the encoder to generate the initial estimation by a parametric method, which aims to provide the prior knowledge of the human hand for subsequent processes. Then, we gradually fuse multi-scale contextual features for local refinement by explicitly integrating global prior information and local visual features. In particular, we introduce a consecutive pixel-aligned feature retrieval module to extract fine-grained information from visual features, thereby achieving pixel-level alignment. Furthermore, we demonstrate that our method can be extended to weakly-supervised learning where only sparse pose annotations are needed, potentially alleviating the burden of meticulous mesh annotation. The effectiveness and robustness of our method are substantiated through both fully- and weakly-supervised experiments, demonstrating superior performance compared to state-of-the-art methods. We plan to release our code at https://github.com/Kun-Gao/P_GLFnet.

A hierarchical and interlamination graph self-attention mechanism-based knowledge graph reasoning architecture

Knowledge Graph (KG) is an essential research field in graph theory, but its inherent incompleteness and sparsity influence its performance in several fields. Knowledge Graph Reasoning (KGR) aims to ameliorate those problems by mining new knowledge from subsistent knowledge. As one of the downstream tasks of KGR, link prediction is of great significance for improving the quality of KG. Recently, the Graph Neural Network (GNN)-based method became the most effective way to achieve the link prediction task. However, it still suffers from problems such as incomplete neighbor and relation-level information aggregation and unstable learning of the entity's features. To improve those issues, a Hierarchical and Interlamination Graph Self-attention Mechanism-based (HIGSM) plug-and-play architecture is proposed for KGR in this paper. It is composed of three-level layers: feature extractor, encoder, and decoder. The feature extractor makes our architecture more effective and stable for the retrieval of new features. The encoder is equipped with a two-stage encoding mechanism accompanied by two mixture-of-expert strategies, which enables our architecture to capture more practical reasoning information to improve prediction accuracy and generalization of the model. The decoder can use existing KGR models and compute the scores of triples in KG. The extensive experimental results and ablation studies on four KGs unambiguously demonstrate the state-of-the-art prediction performance of the proposed HIGSM architecture compared to current GNN-based methods.

Opposition-based optimized max pooled 3D convolutional features for action video retrieval

Opposition-based optimized max pooled 3D convolutional features for action video retrieval

The interplay of cognitive control and feature integration: insights from theta oscillatory dynamics during conflict processing.

Adaptive behavior is fundamental to cognitive control and executive functioning. This study investigates how cognitive control mechanisms and episodic feature retrieval interact to influence adaptiveness, focusing particularly on theta (4 to 8Hz) oscillatory dynamics. We conducted two variations of the Simon task, incorporating response-incompatible, response-compatible, and neutral trials. Experiment 1 demonstrated that cognitive adjustments-specifically, cognitive shielding following incompatible trials and cognitive relaxation following compatible ones-are reflected in midfrontal theta power modulations associated with the Simon effect. Experiment 2 showed that reducing feature overlap between trials leads to less pronounced sequential modulations in behavior and midfrontal theta activity, supporting the hypothesis that cognitive control and feature integration share a common neural mechanism. These findings highlight the interaction of cognitive control processes and episodic feature integration in modulating behavior. The results advocate for hybrid models that combine top-down and bottom-up processes as a comprehensive framework to understand cognitive control dynamics and adaptive behavior.

An Image-Retrieval Method Based on Cross-Hardware Platform Features

Artificial intelligence (AI) models have already achieved great success in fields such as computer vision and natural language processing. However, deploying AI models based on heterogeneous hardware is difficult to ensure accuracy consistency, especially for precision sensitive feature-based image retrieval. In this article, we realize an image-retrieval method based on cross-hardware platform features, aiming to prove that the features of heterogeneous hardware platforms can be mixed, in which the Huawei Atlas 300V and NVIDIA TeslaT4 are used for experiments. First, we compared the decoding differences of heterogeneous hardware, and used CPU software decoding to help hardware decoding improve the decoding success rate. Then, we compared the difference between the Atlas 300V and TeslaT4 chip architectures and tested the differences between the two platform features by calculating feature similarity. In addition, the scaling mode in the pre-processing process was also compared to further analyze the factors affecting feature consistency. Next, the consistency of capture and correlation based on video structure were verified. Finally, the experimental results reveal that the feature results from the TeslaT4 and Atlas 300V can be mixed for image retrieval based on cross-hardware platform features. Consequently, cross-platform image retrieval with low error is realized. Specifically, compared with the Atlas 300V hard and CPU soft decoding, the TeslaT4 hard decoded more than 99% of the image with a decoding pixel maximum difference of +1/−1. From the average of feature similarity, the feature similarity between the Atlas 300V and TeslaT4 exceeds 99%. The difference between the TeslaT4 and Atlas 300V in recall and mAP in feature retrieval is less than 0.1%.

An effective hybrid attention model for crop yield prediction using IoT-based three-phase prediction with an improved sailfish optimizer

Internet of Things (IoT)-based smart agriculture approaches have been adopted to achieve promising results in terms of food productivity. Moreover, high cost and technological complexity are major problems in existing agricultural management systems. Thus, a new IoT-based smart agriculture farming model is implemented in three phases namely crop detection, crop disease detection, and crop yield prediction. In the first phase, the plant leaf images are gathered from the Kaggle source PlantifyDr. The collected images are passed to the deep feature retrieval phase, where the Visual Geometry Group (VGG16) is utilized to calculate feature (f1). Then, a Hybrid Attention-based Crop Type Detection Network (HA-CTDecNet) is used for detecting the crop types, in which the parameters are optimized by Attack Power Improved Sailfish Optimizer (API-SFO). In the second step of crop disease detection phase, the input images are fed to Otsu Thresholding for Abnormality segmentation. The deep attributes from the segmented images are haul out using VGG16 and attain the feature (f2). The extracted features are forwarded to the Hybrid Attention-based Crop Disease Detection Network (HA-CDDecNet), where the constraints are optimized using API-SFO. In the third crop yield prediction phase, the data related to crop location are collected from the crop yield forecast dataset. Then, the collected data and the extracted features (f1) and (f2) are concatenated and fed to the Principle Component Analysis (PCA). Then, these features are given to the Hybrid Attention-based Crop Yield Prediction Network (HA-CYPreNet) with the parameter optimization of API-SFO. The investigation is accepted to verify the effectiveness of the suggested IoT-based smart agriculture model with the traditional approaches by considering distinct measures. The Mean Absolute Error (MAE) of the crop yield prediction model is improved with 35% than CNN, 27.5% than LSTM, 17.5% than GRU, and 7.5% than LSTM_GRU while considering the learning percentage value as 55.

EC-YOLO: Improved YOLOv7 Model for PCB Electronic Component Detection.

Electronic components are the main components of PCBs (printed circuit boards), so the detection and classification of ECs (electronic components) is an important aspect of recycling used PCBs. However, due to the variety and quantity of ECs, traditional target detection methods for EC classification still have problems such as slow detection speed and low performance, and the accuracy of the detection needs to be improved. To overcome these limitations, this study proposes an enhanced YOLO (you only look once) network (EC-YOLOv7) for detecting EC targets. The network uses ACmix (a mixed model that enjoys the benefits of both self-attention and convolution) as a substitute for the 3 × 3 convolutional modules in the E-ELAN (Extended ELAN) architecture and implements branch links and 1 × 1 convolutional arrays between the ACmix modules to improve the speed of feature retrieval and network inference. Furthermore, the ResNet-ACmix module is engineered to prevent the leakage of function data and to minimise calculation time. Subsequently, the SPPCSPS (spatial pyramid pooling connected spatial pyramid convolution) block has been improved by replacing the serial channels with concurrent channels, which improves the fusion speed of the image features. To effectively capture spatial information and improve detection accuracy, the DyHead (the dynamic head) is utilised to enhance the model's size, mission, and sense of space, which effectively captures spatial information and improves the detection accuracy. A new bounding-box loss regression method, the WIoU-Soft-NMS method, is finally suggested to facilitate prediction regression and improve the localisation accuracy. The experimental results demonstrate that the enhanced YOLOv7 net surpasses the initial YOLOv7 model and other common EC detection methods. The proposed EC-YOLOv7 network reaches a mean accuracy (mAP@0.5) of 94.4% on the PCB dataset and exhibits higher FPS compared to the original YOLOv7 model. In conclusion, it can significantly enhance high-density EC target recognition.

Data-driven retrieval of population-level EEG features and their role in neurodegenerative diseases.

Electrophysiologic disturbances due to neurodegenerative disorders such as Alzheimer's disease and Lewy Body disease are detectable by scalp EEG and can serve as a functional measure of disease severity. Traditional quantitative methods of EEG analysis often require an a-priori selection of clinically meaningful EEG features and are susceptible to bias, limiting the clinical utility of routine EEGs in the diagnosis and management of neurodegenerative disorders. We present a data-driven tensor decomposition approach to extract the top 6 spectral and spatial features representing commonly known sources of EEG activity during eyes-closed wakefulness. As part of their neurologic evaluation at Mayo Clinic, 11 001 patients underwent 12 176 routine, standard 10-20 scalp EEG studies. From these raw EEGs, we developed an algorithm based on posterior alpha activity and eye movement to automatically select awake-eyes-closed epochs and estimated average spectral power density (SPD) between 1 and 45 Hz for each channel. We then created a three-dimensional (3D) tensor (record × channel × frequency) and applied a canonical polyadic decomposition to extract the top six factors. We further identified an independent cohort of patients meeting consensus criteria for mild cognitive impairment (30) or dementia (39) due to Alzheimer's disease and dementia with Lewy Bodies (31) and similarly aged cognitively normal controls (36). We evaluated the ability of the six factors in differentiating these subgroups using a Naïve Bayes classification approach and assessed for linear associations between factor loadings and Kokmen short test of mental status scores, fluorodeoxyglucose (FDG) PET uptake ratios and CSF Alzheimer's Disease biomarker measures. Factors represented biologically meaningful brain activities including posterior alpha rhythm, anterior delta/theta rhythms and centroparietal beta, which correlated with patient age and EEG dysrhythmia grade. These factors were also able to distinguish patients from controls with a moderate to high degree of accuracy (Area Under the Curve (AUC) 0.59-0.91) and Alzheimer's disease dementia from dementia with Lewy Bodies (AUC 0.61). Furthermore, relevant EEG features correlated with cognitive test performance, PET metabolism and CSF AB42 measures in the Alzheimer's subgroup. This study demonstrates that data-driven approaches can extract biologically meaningful features from population-level clinical EEGs without artefact rejection or a-priori selection of channels or frequency bands. With continued development, such data-driven methods may improve the clinical utility of EEG in memory care by assisting in early identification of mild cognitive impairment and differentiating between different neurodegenerative causes of cognitive impairment.

Novel Content Based Image Retrieval—Features of Correlated Visual Textons and MQLPP Descriptor

Novel Content Based Image Retrieval—Features of Correlated Visual Textons and MQLPP Descriptor

Outlier classification for microbiological open set recognition

Precise detection and effective intervention of pathogenic microorganisms are pivotal to ensure crop yield. Traditional open set recognition methods distinguish unknown classes, i.e., those not involved in training, as outliers or separate classes, which is insufficient for real-world applications like recognizing airborne microorganisms and particles. In this paper, a new setting for the classification of outliers for open set recognition is proposed. The proposed method classifies unknown classes while achieving the classification of known classes, treating each category in the open set equally. The two cores of the method are hierarchical feature representation and feature gallery construction. To enhance the feature representation, this paper proposes a novel architectural module of spatial attention on region hierarchy, namely SPARC. This module extracts features from hierarchical levels and models spatial interaction through the attention mechanism. With the induction of local-to-global description, minute differences with ambiguous appearances between classes can be detected, and the negative effects of image quality degradation can be ameliorated. Furthermore, a feature gallery containing the feature representation from both known and unknown classes is developed for feature retrieval. To demonstrate the effectiveness of the proposed model, the first dataset composed of agricultural microorganisms, named SMAD, is constructed, and experiments are conducted on existing datasets and the newly proposed SMAD dataset. The experimental results outperform the prior art on these datasets under both the outlier rejection and outlier classification settings.

Neural correlates of retrieval success and precision: an fMRI study.

Prior studies examining the neural mechanisms underlying retrieval success and precision have yielded inconsistent results. Here, their neural correlates were examined using a memory task that assessed precision for spatial location. A sample of healthy young adults underwent fMRI scanning during a single study-test cycle. At study, participants viewed a series of object images, each placed at a randomly selected location on an imaginary circle. At test, studied images were intermixed with new images and presented to the participants. The requirement was to move a cursor to the location of the studied image, guessing if necessary. Participants then signaled whether the presented image as having been studied. Memory precision was quantified as the angle between the studied location and the location selected by the participant. A precision effect was evident in the left angular gyrus, where BOLD activity covaried across trials with location accuracy. Multi-voxel pattern analysis also revealed a significant item-level reinstatement effect for high-precision trials. There was no evidence of a retrieval success effect in the angular gyrus. BOLD activity in the hippocampus was insensitive to both success and precision. These findings are partially consistent with prior evidence that success and precision are dissociable features of memory retrieval.

Parallel Hierarchical Multi-View Feature Fusion Based on Canonical Correlation Analysis for Mammogram Retrieval

Due to the diversity of image sources, content-based multi-source image fusion and retrieval have shown promising capabilities in computer vision tasks, and especially when applied in Computer-Aided Diagnosis (CAD) to automate and improve the accuracy of medical image analysis. The combination of computer vision and CAD systems has the potential to revolutionize healthcare by augmenting the expertise of clinicians, improving overall diagnostic accuracy and helping experts in the clinical decision-making process by classifying and retrieving similar annotated clinical images to a given query. In the context of multi-view mammography interpretation, the concept of multi-view feature fusion has recently been studied to improve retrieval performance while effectively guaranteeing the complementarity of both MLO and CC views. However, conventional multi-view feature fusion makes descriptors long and lacks to take into consideration the relationship between descriptors. To deal with this issue, we propose two hierarchical multi-view feature fusion methods, for multi-view mammogram retrieval, based on the Canonical Correlation Analysis (CCA), which is the most commonly used multivariate parametric test. In fact, we have adapted CCA to determine the relationship between two descriptors by processing latent correlation factors. Moreover, after extracting descriptors for each view, a comparative study of texture and shape fusion descriptors is proposed in order to identify the more discriminative features for multi-view mammogram retrieval. Then, a query-dependent distance metric preserving both visual resemblance and semantic similarity is carried out to dynamically determine the more appropriate distance measure for each query image. Extensive experiments on the challenging Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) have demonstrated the effectiveness of the proposed hierarchical multi-view feature fusion for mammogram retrieval, which outperforms the performance achieved either by conventional fused information or by single view information. To improve the transparency of our paper, the source code of the proposed method and the related dataset (including readme files) are publicly accessible through the following GitHub link: https://github.com/ABDERRAHIMMAR/Multi-View-Feature-Fusion-for-Mammogram-Retrieval . This open-access resource empowers researchers and practitioners to delve deeper into our methodology, fostering collaboration and advancements in the field of computer-aided diagnosis and medical image analysis.

On-the-Fly SfM: What you capture is What you get

Abstract. Over the last decades, ample achievements have been made on Structure from Motion (SfM). However, the vast majority of them basically work in an offline manner, i.e., images are firstly captured and then fed together into a SfM pipeline for obtaining poses and sparse point cloud. In this work, on the contrary, we present an on-the-fly SfM: running online SfM while image capturing, the newly taken On-the-Fly image is online estimated with the corresponding pose and points, i.e., what you capture is what you get. Specifically, our approach firstly employs a vocabulary tree that is unsupervised trained using learning-based global features for fast image retrieval of newly fly-in image. Then, a robust feature matching mechanism with least squares (LSM) is presented to improve image registration performance. Finally, via investigating the influence of newly fly-in image’s connected neighboring images, an efficient hierarchical weighted local bundle adjustment (BA) is used for optimization. Extensive experimental results demonstrate that on-the-fly SfM can meet the goal of robustly registering the images while capturing in an online way.

Patch-based tendency camera multi-constraint learning for unsupervised person re-identification

Unsupervised person re-identification (ReID) is a task that aims to retrieve pedestrians across different cameras from unlabeled data. Existing methods rely on clustering to generate pseudo-labels, but they are inevitably noisy. Although pseudo-label refinement approaches have been presented, the essentiality of patch contours is ignored. The tendency analysis of retrieval between global and patch features has not been well investigated. In this paper, we propose a Patch-based Tendency Camera Multi-Constraint Learning (PTCML) model for unsupervised person ReID. First, to explore the tendentious retrieval of global and patch features, we design a Ranking Tendency Similarity (RTS) score by gauging the distribution discrepancy of distance changes. Second, based on RTS score, we propose a Tendency-based Mutual Complementation (TMC) loss to improve the quality of global and patch pseudo-labels. Third, to resist camera variations, we propose an Adaptive Camera Multi-Constraint (ACM) loss to optimize recognition results with camera distribution constraint and instance constraint simultaneously. Finally, numerous experiments on Market-1501 and MSMT17 demonstrate that our method can significantly surpass the state-of-the-art performance.