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CISS: An efficient object clustering framework for DHT-based peer-to-peer applications

COMPUTER NETWORKS 2007年第4期51卷 1072-1094页

作者： Lee, Jinwon Lee, Hyonik Kang, Seungwoo Kim, Su Myeon Song, Junehwa Korea Adv Inst Sci & Technol Div Comp Sci Dept EECS Network Comp Lab Taejon South Korea

In most DHT-based peer-to-peer systems, objects are totally declustered since such systems use a hash function to distribute objects evenly. However, such an object de-clustering can result in significant inefficiencies in advanced access operations such as multi-dimensional range queries, continuous updates, etc, which are common in many emerging peer-to-peer applications. In this paper, we propose CISS (Cooperative Information Sharing System), a framework that supports efficient object clustering for DHT-based peer-to-peer applications. CISS uses a Locality Preserving Function (LPF) instead of a hash function, thereby achieving a high level of clustering without requiring any changes to existing DHT implementations. To maximize the benefit of object clustering, CISS provides efficient routing protocols for multi-dimensional range queries and continuous updates. Furthermore, our cluster-preserving load balancing schemes distribute loads without hot-spots while preserving the object clustering property. We demonstrate the performance benefits of CISS through extensive simulation. (c) 2006 Elsevier B.V. All rights reserved.

关键词： distributed hash table (DHT) object clustering peer-to-peer application multi-dimensional range query load balancing

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Generalised heat kernel invariants of a graph and application to object clustering

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2024年 167卷 264-285页

作者： Mutombo, Franck Kalala Nanyanzi, Alice Univ Lubumbashi Dept Math & Comp Sci Route Kasapa 1825 Lubumbashi DEM REP CONGO African Inst Math Sci AIMS South Africa 5-6 Melrose Rd ZA-7945 Muizenberg South Africa

In recent years, the heat kernel has proven to be a valuable tool for graph characterization and graph -based object clustering. It serves as the fundamental solution to the heat diffusion equation associated with the discrete graph Laplacian, describing how information flows across graph edges over time by exponentiating the Laplacian eigensystem. This paper focuses on the novel concept of the generalized heat kernel of a network, recently introduced by Kalala Mutombo et al. These authors build upon the k -path Laplacian operator for graphs, pioneered by Estrada et al., to incorporate long-range interactions (LRI) into information transmission across nodes and edges of a network/graph. LRI are handled through the use of Mellin and Laplace transforms. This paper makes a notable contribution by highlighting the practical application of the generalized heat kernel. We achieve this by demonstrating the effectiveness of the generalised heat kernel invariants in object clustering with real data using principal component analysis (PCA). Moreover, we investigate how incorporating longrange interactions (LRI) impacts object characterization and clustering, revealing superior results compared to conventional diffusion methods. Through experimentation, we show that object clustering remains achievable even with small values of the Mellin and Laplace parameters, contrasting with the requirement of an infinite value in the absence of LRI.

关键词： Generalised heat kernel Mellin and Laplace transforms Long-range interactions Generalised heat content object clustering Principal component analyses

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Learning a Mixture of Sparse Models by EM Algorithm for object clustering 4

Learning a Mixture of Sparse Models by EM Algorithm for Obje...

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4th International Conference on Computer Science and Network Technology (ICCSNT)

作者： Fang, Yuhan Jiang, Ruojing Li, Chenguang Southwest Univ Coll Elect & Informat Engn Chongqing Peoples R China Fudan Univ Sch Math Sci Shanghai Peoples R China Hefei Univ Technol Dept Informat Engn Xuancheng Peoples R China

ISBN: (纸本)9781467381734

Model-based object clustering is a very challenging unsupervised learning problem in computer vision, which involves both high dimensionality and hidden variables inference issues. In this paper, we will study object pattern clustering problem by using the Active basis model, which is a sparse representation model for object patterns. We fit a mixture of active basis models, which leads to an object clustering result, as well as the inference of all the hidden variables in the object clustering problem. This strategy not only gives us a way to represent each object by a sparse model, but also an elegant solution to the clustering problem with hidden variables, such as unknown locations, scales, and orientations of the objects appearing in the images. The experiment conducted on a small clustering dataset shows that learning a mixture of active basis models by EM-like algorithm for object clustering is very promising.

关键词： object clustering Active basis model EM algorithm Computer vision Mixture model

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Learning a Mixture of Sparse Models by EM Algorithm for object clustering

Learning a Mixture of Sparse Models by EM Algorithm for Obje...

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International Conference on Computer Science and Network Technology

作者： Yuhan Fang Ruojing Jiang Chenguang Li College of Electronic and Information Engineering Southwest University Chongqing China School of Mathematical Sciences Fudan University Shanghai China Department of Information Engineering Hefei University of Technology Xuancheng China

ISBN: (纸本)9781467381741

关键词： object clustering Active basis model EM algorithm Computer vision Mixture model expectation-maximisation algorithm images computer vision Cluster Analysis Mixture models mixtures HIDDEN VARIABLES sparse representation

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Xu, Jing Okada, Shogo Nitta, Katsumi Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Tokyo 152 Japan

ISBN: (纸本)9781479906529

object recognition and clustering are useful techniques in pattern recognition and computer vision. Traditionally, these techniques have been implemented by visual-feature-based methods. However, these methods may not adequately tackle the differences in the shapes and colors of objects. In this paper, we propose an alternative method in which objects of different colors, or even different shapes, function similarly. If text strings are visible on their surfaces, we can extract the semantic features of objects, thereby recognizing and clustering them. Thus, this method is based on semantic information. The method is experimentally tested on a dataset of images containing the packing cases of commercial products. Semantic information in the dataset images is retrieved using text extraction modules, passed through an Internet data mining module and is finally described and clustered. The final clustering results are more accurate than those obtained by visual-feature-based methods.

关键词： object Recognition image processing word mining object clustering

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Latent Semantic Analysis (LSA) Based object Recognition and clustering 1

Latent Semantic Analysis (LSA) Based Object Recognition and ...

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International Conference on Green Computing and Internet of Things (ICGCIoT)

作者： Hebballi, Vinaykumar Rojit, Vidhu PES Inst Technol Dept Comp Sci Bangalore Karnataka India

ISBN: (纸本)9781467379106

object Recognition and clustering are major techniques in Pattern Recognition, Computer Vision, Artificial Intelligence and Robotics. Conventionally these techniques are implemented in Visual-Feature based methods and Cosine Similarity method or Vector Space method which uses semantic similarity among the objects to solve these kinds of problem, but this method has two problems synonymy and polysemy. In synonymy many ways to refer to the same objects for e.g. car and automobile, which leads to poor recall. Similarly, in polysemy most words have more than one meaning, e.g. model, python and chip, which leads to poor precision. In this paper, we propose a method which will overcome the problems like synonymy and polysemy. If the text printed on the object the semantic feature of that object is extracted and clustered according to Latent Semantic Analysis (LSA). The Proposed method is based on semantic information so an experiment is conducted with the dataset of images which contains the packing cases of commercial products (e.g. Mobile, Laptop etc). Semantic information in the dataset is retrieved using text extraction modules and then the results of text extraction are passed through an Internet search module. Finally, objects are recognized and clustered using the LSA module. The clustering results are more accurate than cosine similarity or vector space method.

关键词： object Recognition Image Processing Text Mining object clustering

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Jing Xu Shogo Okada Katsumi Nitta Department of Computational Intelligence and Systems Science Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9781479906505

关键词： object Recognition image processing word mining object clustering

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ChatNav: Leveraging LLM to Zero-Shot Semantic Reasoning in object Navigation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第3期35卷 2369-2381页

作者： Zhu, Yong Wen, Zhenyu Li, Xiong Shi, Xiufang Wu, Xiang Dong, Hui Chen, Jiming Zhejiang Univ Technol Inst Cyberspace Secur Hangzhou 310023 Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Hangzhou Dianzi Univ Hangzhou 310018 Peoples R China

In object goal navigation tasks, the robot's understanding of semantic relationships in the environment is a key factor in its ability to localize target objects. Previously, learning-based methods trained robots using 3D scene datasets to learn semantic relationships. However, these approaches perform poorly in new environments with unfamiliar semantic contexts. In this paper, we propose ChatNav which leverages the powerful knowledge summarizing and reasoning capabilities of a Large Language Model (LLM) for zero-shot inference of explicit semantic relationships. These relationships are further integrated into the navigation system for efficient localization of target objects. ChatNav employs a spatial object clustering algorithm to collect semantic clues and designs common-sense-based prompts for interacting with LLM. It then uses a gravity-repulsion model to convert inference results into heuristic factors for robust navigation decision-making. Our approach requires no additional training and can consistently obtain accurate semantic relationships from LLM, making it well-suited for navigating unknown environments. Experimental results demonstrate the outstanding navigation performance of our proposed method on the Gibson and HM3D datasets, surpassing the current state-of-the-art object goal navigation methods.

关键词： Semantics Navigation Robots Cognition TV Accuracy Chatbots Large language models Decision making Pipelines object goal navigation LLM object clustering prompt gravity-repulsion model

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Genetic algorithms for clustering in machine vision

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MACHINE VISION AND APPLICATIONS 1998年第1期11卷 1-6页

作者： Cucchiara, R Univ Ferrara Dipartimento Ingn I-44100 Ferrara Italy

The paper presents a genetic algorithm for clustering objects in images based on their visual features. In particular, a novel solution code (named Boolean Matching Code) and a correspondent reproduction operator (the Single Gene Crossover) are defined specifically for clustering and are compared with other standard genetic approaches. The paper describes the clustering algorithm in detail, in order to show the suitability of the genetic paradigm and underline the importance of effective tuning of algorithm parameters to the application. The algorithm is evaluated on some test sets and an example of its application in automated visual inspection is presented.

关键词： genetic algorithms object clustering visual features fitness function automated visual inspection

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Spatial Context-Aware object-Attentional Network for Multi-Label Image Classification

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 3000-3012页

作者： Zhang, Jialu Ren, Jianfeng Zhang, Qian Liu, Jiang Jiang, Xudong Univ Nottingham Ningbo China Sch Comp Sci Ningbo 315100 Zhejiang Peoples R China Chinese Acad Sci Cixi Inst Biomed Engn Ningbo 315201 Zhejiang Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Guangdong Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Multi-label image classification is a fundamental but challenging task in computer vision. To tackle the problem, the label-related semantic information is often exploited, but the background context and spatial semantic information of related objects are not fully utilized. To address these issues, a multi-branch deep neural network is proposed in this paper. The first branch is designed to extract the discriminant information from regions of interest to detect target objects. In the second branch, a spatial context-aware approach is proposed to better capture the contextual information of an object in its surroundings by using an adaptive patch expansion mechanism. It helps the detection of small objects that are easily lost without the support of context information. The third one, the object-attentional branch, exploits the spatial semantic relations between the target object and its related objects, to better detect partially occluded, small or dim objects with the support of those easily detectable objects. To better encode such relations, an attention mechanism jointly considering the spatial and semantic relations between objects is developed. Two widely used benchmark datasets for multi-labeling classification, MS COCO and PASCAL VOC, are used to evaluate the proposed framework. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods for multi-label image classification.

关键词： Semantics Image classification Task analysis Feature extraction Context modeling object detection Correlation Multi-label image classification adaptive patch expansion spatial context-aware object detection object clustering spatial semantic attention

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