Hybrid Query Refinement

2011 ◽  
Vol 2 (3) ◽  
pp. 52-71
Author(s):  
Kasturi Chatterjee ◽  
Shu-Ching Chen
Keyword(s):  
Nearest Neighbor ◽  
Feature Space ◽  
Semantic Space ◽  
Index Structure ◽  
K Nearest Neighbor ◽  
Query Refinement ◽  
Low Level ◽  
Computational Overhead ◽  
Query Result ◽  
High Level

This paper proposes a hybrid query refinement model for distance-based index structures supporting content-based image retrievals. The framework refines a query by considering both the low-level feature space as well as the high-level semantic interpretations separately. Thus, it successfully handles queries where the gap between the feature components and the semantics is large. It refines the low-level feature space, indexed by the distance based index structure, in multiple iterations by introducing the concept of multipoint query in a metric space. It refines the high-level semantic space by dynamically adjusting the constructs of a framework, called the Markov Model Mediator (MMM), utilized to introduce the semantic relationships in the index structure. A k-nearest neighbor (k-NN) algorithm is designed to handle similarity searches that refine a query in multiple iterations utilizing the proposed hybrid query refinement model. Extensive experiments are performed demonstrating an increased relevance of query results in subsequent iterations while incurring a low computational overhead. Further, an evaluation metric, called the Model_Score, is proposed to compare the performance of different retrieval frameworks in terms of both computation overhead and query result relevance. This metric enables the users to choose the retrieval framework appropriate for their requirements.

Hybrid Query Refinement

2013 ◽  
pp. 131-150
Author(s):  
Kasturi Chatterjee ◽  
Shu-Ching Chen
Keyword(s):  
Nearest Neighbor ◽  
Feature Space ◽  
Index Structure ◽  
K Nearest Neighbor ◽  
Query Refinement ◽  
Low Level ◽  
Computational Overhead ◽  
Query Result ◽  
Evaluation Metric ◽  
High Level

This paper proposes a hybrid query refinement model for distance-based index structures supporting content-based image retrievals. The framework refines a query by considering both the low-level feature space as well as the high-level semantic interpretations separately. Thus, it successfully handles queries where the gap between the feature components and the semantics is large. It refines the low-level feature space, indexed by the distance based index structure, in multiple iterations by introducing the concept of multipoint query in a metric space. It refines the high-level semantic space by dynamically adjusting the constructs of a framework, called the Markov Model Mediator (MMM), utilized to introduce the semantic relationships in the index structure. A k-nearest neighbor (k-NN) algorithm is designed to handle similarity searches that refine a query in multiple iterations utilizing the proposed hybrid query refinement model. Extensive experiments are performed demonstrating an increased relevance of query results in subsequent iterations while incurring a low computational overhead. Further, an evaluation metric, called the Model_Score, is proposed to compare the performance of different retrieval frameworks in terms of both computation overhead and query result relevance. This metric enables the users to choose the retrieval framework appropriate for their requirements.

A NOVEL INDEXING AND ACCESS MECHANISM USING AFFINITY HYBRID TREE FOR CONTENT-BASED IMAGE RETRIEVAL IN MULTIMEDIA DATABASES

2007 ◽  
Vol 01 (02) ◽  
pp. 147-170 ◽  
Author(s):  
KASTURI CHATTERJEE ◽  
SHU-CHING CHEN
Keyword(s):  
Image Retrieval ◽  
Metric Spaces ◽  
Nearest Neighbor ◽  
Human Perception ◽  
Multimedia Databases ◽  
Multimedia Retrieval ◽  
Index Structure ◽  
Content Based Image Retrieval ◽  
K Nearest Neighbor ◽  
Computational Overhead

An efficient access and indexing framework, called Affinity Hybrid Tree (AH-Tree), is proposed which combines feature and metric spaces in a novel way. The proposed framework helps to organize large image databases and support popular multimedia retrieval mechanisms like Content-Based Image Retrieval (CBIR). It is efficient in terms of computational overhead and fairly accurate in producing query results close to human perception. AH-Tree, by being able to introduce the high level semantic image relationship as it is in its index structure, solves the problem of translating the content-similarity measurement into feature level equivalence which is both painstaking and error-prone. Algorithms for similarity (range and k-nearest neighbor) queries are implemented and extensive experiments are performed which produces encouraging results with low I/O and distance computations and high precision of query results.

Event Detection in Sports Video Based on Generative-Discriminative Models

2011 ◽  
pp. 143-165
Author(s):  
Guoliang Fan ◽  
Yi Ding
Keyword(s):  
Event Detection ◽  
Semantic Analysis ◽  
Building Blocks ◽  
Semantic Space ◽  
Sports Video ◽  
Low Level ◽  
Discriminative Models ◽  
Video Mining ◽  
High Level ◽  
Different Levels

Semantic event detection is an active and interesting research topic in the field of video mining. The major challenge is the semantic gap between low-level features and high-level semantics. In this chapter, we will advance a new sports video mining framework where a hybrid generative-discriminative approach is used for event detection. Specifically, we propose a three-layer semantic space by which event detection is converted into two inter-related statistical inference procedures that involve semantic analysis at different levels. The first is to infer the mid-level semantic structures from the low-level visual features via generative models, which can serve as building blocks of high-level semantic analysis. The second is to detect high-level semantics from mid-level semantic structures using discriminative models, which are of direct interests to users. In this framework we can explicitly represent and detect semantics at different levels. The use of generative and discriminative approaches in two different stages is proved to be effective and appropriate for event detection in sports video. The experimental results from a set of American football video data demonstrate that the proposed framework offers promising results compared with traditional approaches.

scholarly journals KLASIFIKASI PENGGUNAAN PROTOKOL KOMUNIKASI PADA TRAFIK JARINGAN MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR

2016 ◽  
Vol 16 (1) ◽  
pp. 67
Author(s):  
Komang Kompyang Agus Subrata ◽  
I Made Oka Widyantara ◽  
Linawati Linawati
Keyword(s):  
Quality Of Service ◽  
Network Traffic ◽  
Network Architecture ◽  
Nearest Neighbor ◽  
Computer Network ◽  
Data Communication ◽  
Data Capture ◽  
K Nearest Neighbor ◽  
High Level

ABSTRACT—Network traffic internet is data communication in a network characterized by a set of statistical flow with the application of a structured pattern. Structured pattern in question is the information from the packet header data. Proper classification to an Internet traffic is very important to do, especially in terms of the design of the network architecture, network management and network security. The analysis of computer network traffic is one way to know the use of the computer network communication protocol, so it can be the basis for determining the priority of Quality of Service (QoS). QoS is the basis for giving priority to analyzing the network traffic data. In this study the classification of the data capture network traffic that though the use of K-Neaerest Neighbor algorithm (K-NN). Tools used to capture network traffic that wireshark application. From the observation of the dataset and the network traffic through the calculation process using K-NN algorithm obtained a result that the value generated by the K-NN classification has a very high level of accuracy. This is evidenced by the results of calculations which reached 99.14%, ie by calculating k = 3. Intisari—Trafik jaringan internet adalah lalu lintas ko­mu­nikasi data dalam jaringan yang ditandai dengan satu set ali­ran statistik dengan penerapan pola terstruktur. Pola ter­struktur yang dimaksud adalah informasi dari header paket data. Klasifikasi yang tepat terhadap sebuah trafik internet sa­ngat penting dilakukan terutama dalam hal disain perancangan arsitektur jaringan, manajemen jaringan dan keamanan jari­ngan. Analisa terhadap suatu trafik jaringan komputer meru­pakan salah satu cara mengetahui penggunaan protokol komu­nikasi jaringan komputer, sehingga dapat menjadi dasar pe­nen­tuan prioritas Quality of Service (QoS). Dasar pemberian prio­ritas QoS adalah dengan penganalisaan terhadap data trafik jaringan. Pada penelitian ini melakukan klasifikasi ter­hadap data capture trafik jaringan yang di olah menggunakan Algoritma K-Neaerest Neighbor (K-NN). Apli­kasi yang digu­nakan untuk capture trafik jaringan yaitu aplikasi wireshark. Hasil observasi terhadap dataset trafik jaringan dan melalui proses perhitungan menggunakan Algoritma K-NN didapatkan sebuah hasil bahwa nilai yang dihasilkan oleh klasifikasi K-NN memiliki tingkat keakuratan yang sangat tinggi. Hal ini dibuktikan dengan hasil perhi­tungan yang mencapai nilai 99,14 % yaitu dengan perhitungan k = 3. DOI: 10.24843/MITE.1601.10

scholarly journals k-Nearest Neighbor Search based on Node Density in MANETs

2014 ◽  
Vol 10 (4) ◽  
pp. 385-405 ◽  
Author(s):  
Yuka Komai ◽  
Yuya Sasaki ◽  
Takahiro Hara ◽  
Shojiro Nishio
Keyword(s):  
Query Processing ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
K Nearest Neighbor ◽  
Node Density ◽  
Neighbor Search ◽  
Query Result ◽  
Knn Query ◽  
The One ◽  
K Nearest Neighbor Search

In a kNN query processing method, it is important to appropriately estimate the range that includes kNNs. While the range could be estimated based on the node density in the entire network, it is not always appropriate because the density of nodes in the network is not uniform. In this paper, we propose two kNN query processing methods in MANETs where the density of nodes is ununiform; the One-Hop (OH) method and the Query Log (QL) method. In the OH method, the nearest node from the point specified by the query acquires its neighbors' location and then determines the size of a circle region (the estimated kNN circle) which includes kNNs with high probability. In the QL method, a node which relays a reply of a kNN query stores the information on the query result for future queries.

A Survey on Feature Selection Based Spam Review Detection using Deep Learning Techniques

2020 ◽  
pp. 102-108
Author(s):  
Sophia S ◽  
Rajamohana SP
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Nearest Neighbor ◽  
Feature Space ◽  
Online Reviews ◽  
High Dimensionality ◽  
Product Reviews ◽  
K Nearest Neighbor ◽  
Good Decision ◽  
Learning Techniques

In recent times, online shoppers are technically knowledgeable and open to product reviews. They usually read the buyer reviews and ratings before purchasing any product from ecommerce website. For the better understanding of products or services, reviews provided by the customers gives the vital source of information. In order to buy the right products for the individuals and to make the business decisions for the Organization online reviews are very important. These reviews or opinions in turn, allow us to find out the strength and weakness of the products. Spam reviews are written in order to falsely promote or demote a few target products or services. Also, detecting the spam reviews has also become more critical issue for the customer to make good decision during the purchase of the product. A major problem in identifying the fake review detection is high dimensionality of the feature space. Therefore, feature selection is an essential step in the fake review detection to reduce dimensionality of the feature space and to improve the classification accuracy. Hence it is important to detect the spam reviews but the major issues in spam review detection are the high dimensionality of feature space which contains redundant, noisy and irrelevant features. To resolve this, Deep Learning Techniques for selecting features is necessary. To classify the features, classifiers such as Naive Bayes, K Nearest Neighbor are used. An analysis of the various techniques employed to identify false and genuine reviews has been surveyed.

scholarly journals Health Monitoring for Balancing Tail Ropes of a Hoisting System Using a Convolutional Neural Network

2018 ◽  
Vol 8 (8) ◽  
pp. 1346 ◽  
Author(s):  
Ping Zhou ◽  
Gongbo Zhou ◽  
Zhencai Zhu ◽  
Chaoquan Tang ◽  
Zhenzhi He ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Health Monitoring ◽  
Nearest Neighbor ◽  
Back Propagation ◽  
Feature Space ◽  
Batch Size ◽  
K Nearest Neighbor ◽  
Hoisting System

With the arrival of the big data era, it has become possible to apply deep learning to the health monitoring of mine production. In this paper, a convolutional neural network (CNN)-based method is proposed to monitor the health condition of the balancing tail ropes (BTRs) of the hoisting system, in which the feature of the BTR image is adaptively extracted using a CNN. This method can automatically detect various BTR faults in real-time, including disproportional spacing, twisted rope, broken strand and broken rope faults. Firstly, a CNN structure is proposed, and regularization technology is adopted to prevent overfitting. Then, a method of image dataset description and establishment that can cover the entire feature space of overhanging BTRs is put forward. Finally, the CNN and two traditional data mining algorithms, namely, k-nearest neighbor (KNN) and an artificial neural network with back propagation (ANN-BP), are adopted to train and test the established dataset, and the influence of hyperparameters on the network diagnostic accuracy is investigated experimentally. The experimental results showed that the CNN could effectively avoid complex steps such as manual feature extraction, that the learning rate and batch-size strongly affected the accuracy and training efficiency, and that the fault diagnosis accuracy of CNN was 100%, which was higher than that of KNN and ANN-BP. Therefore, the proposed CNN with high accuracy, real-time functioning and generalization performance is suitable for application in the health monitoring of hoisting system BTRs.

scholarly journals Character Recognition And Extraction of An Indian State License Plates Using K-NN

2019 ◽  
Author(s):  
Rajasekhar Ponakala ◽  
Hari Krishna Adda ◽  
Ch. Aravind Kumar ◽  
Kavya Avula ◽  
K. Anitha Sheela
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Nearest Neighbor ◽  
Extraction Process ◽  
License Plate ◽  
K Nearest Neighbor ◽  
Open Hardware ◽  
Character Extraction ◽  
Indian State ◽  
High Level

License plate recognition is an application-specific optimization in Optical Character Recognition (OCR) software which enables computer systems to read automatically the License Plates of vehicles from digital images. This thesis discusses the character extraction from the respective License Plates of vehicles and problems in the character extraction process. An OCR based training algorithm named k-nearest neighbor with predefined OpenCV libraries is implemented and evaluated in the BeagleBone Black Open Hardware. In an OCR, the character extraction involves certain steps which include Image acquisition, Pre-processing, Feature extraction, Detection/ Segmentation, High-level processing, Decision making. A key advantage of the method is that it is a fairly straightforward technique which utilizes from k-nearest neighbor algorithm segments normalized result as a format in text. The results show that training an image with this algorithm gives better results when compared with other algorithms.

KNN Loss and Deep KNN

2021 ◽  
Vol 182 (2) ◽  
pp. 95-110
Author(s):  
Linh Le ◽  
Ying Xie ◽  
Vijay V. Raghavan
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Nearest Neighbor ◽  
Learning Strategy ◽  
Feature Space ◽  
Kernel Functions ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Map Data

The k Nearest Neighbor (KNN) algorithm has been widely applied in various supervised learning tasks due to its simplicity and effectiveness. However, the quality of KNN decision making is directly affected by the quality of the neighborhoods in the modeling space. Efforts have been made to map data to a better feature space either implicitly with kernel functions, or explicitly through learning linear or nonlinear transformations. However, all these methods use pre-determined distance or similarity functions, which may limit their learning capacity. In this paper, we present two loss functions, namely KNN Loss and Fuzzy KNN Loss, to quantify the quality of neighborhoods formed by KNN with respect to supervised learning, such that minimizing the loss function on the training data leads to maximizing KNN decision accuracy on the training data. We further present a deep learning strategy that is able to learn, by minimizing KNN loss, pairwise similarities of data that implicitly maps data to a feature space where the quality of KNN neighborhoods is optimized. Experimental results show that this deep learning strategy (denoted as Deep KNN) outperforms state-of-the-art supervised learning methods on multiple benchmark data sets.

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