Performance analysis of tabular nearest-neighbor encoding for joint image compression and ATR: I. Background and theory [3814-15] (2024)

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Key, G. / Schmalz, M. S. / Caimi, F. M. / SPIE

Neue Suche nach: Key, G.

Neue Suche nach: Schmalz, M. S.

Neue Suche nach: Caimi, F. M.

Neue Suche nach: SPIE

Neue Suche nach: Key, G.

Neue Suche nach: Schmalz, M. S.

Neue Suche nach: Caimi, F. M.

Neue Suche nach: Schmalz, M. S.

Neue Suche nach: SPIE

In: Mathematics of data/image coding, compression, and encryption ; 115-126 ; 1999

ISBN:

0819433004

ISSN:

0277-786X

Aufsatz (Konferenz) / Print

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Titel:

Performance analysis of tabular nearest-neighbor encoding for joint image compression and ATR: I. Background and theory [3814-15]
Beteiligte:

Key, G. ( Autor:in ) / Schmalz, M. S. ( Autor:in ) / Caimi, F. M. ( Autor:in ) / Schmalz, M. S. / SPIE
Kongress:

Conference; 2nd, Mathematics of data/image coding, compression, and encryption ; 1999 ; Denver, CO
Erschienen in:

Mathematics of data/image coding, compression, and encryption ; 115-126

PROCEEDINGS- SPIE THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING ; 3814 ; 115-126
Verlag:

SPIE

Neue Suche nach: SPIE

Erscheinungsdatum:

01.01.1999
Format / Umfang:

12 pages
ISBN:

0819433004
ISSN:

0277-786X
Medientyp:

Aufsatz (Konferenz)
Format:

Print
Sprache:

Englisch
See Also
Die besten Download-Manager für Windows 10 - WinTotal.de
Schlagwörter:

mathematics , data/image coding , compression
Datenquelle:

British Library Conference Proceedings

Inhaltsverzeichnis Konferenzband

Die Inhaltsverzeichnisse werden automatisch erzeugt und basieren auf den im Index des TIB-Portals verfügbaren Einzelnachweisen der enthaltenen Beiträge. Die Anzeige der Inhaltsverzeichnisse kann daher unvollständig oder lückenhaft sein.

2: Adaptive algorithm for generating optimal bases for digital images
Dreisigmeyer, David / Kirby, Michael J. et al. | 1999

Elektronische Ausgabe
2: Adaptive algorithm for generating optimal bases for digital images [3814-01]
Dreisigmeyer, D. / Kirby, M. J. / SPIE et al. | 1999

Gedruckte Ausgabe
13: Truncated Baker transformation and its extension to image encryption
Miyamoto, Masaki / Tanaka, Kiyoshi / Sugimura, Tatsuo et al. | 1999

Elektronische Ausgabe
13: Truncated Baker transformation and its extension to image encryption [3814-02]
Miyamoto, M. / Tanaka, K. / Sugimura, T. / SPIE et al. | 1999

Gedruckte Ausgabe
26: Information hiding using random sequences [3814-04]
Kim, J.-H. / Kim, K.-T. / Kim, E.-S. / SPIE et al. | 1999

Gedruckte Ausgabe
26: Information hiding using random sequences
Kim, Jang-Hwan / Kim, Kyu-Tae / Kim, Eun-Soo et al. | 1999

Elektronische Ausgabe
36: Transmission of digital chaotic and information-bearing signals in optical communication systems [3814-05]
Gonzalez-Marcos, A. P. / Martin-Pereda, J. A. / SPIE et al. | 1999

Gedruckte Ausgabe
36: Transmission of digital chaotic and information-bearing signals in optical communication systems
Gonzalez-Marcos, Ana P. / Martin-Pereda, Jose A. et al. | 1999

Elektronische Ausgabe
43: Unequal error protection for H.263 video over indoor DECT channel
Abrardo, Andrea / Barni, Mauro / Garzelli, Andrea et al. | 1999

Elektronische Ausgabe
43: Unequal error protection for H.263 video over indoor DECT channel [3814-07]
Abrardo, A. / Barni, M. / Garzelli, A. / SPIE et al. | 1999

Gedruckte Ausgabe
52: Results using an alternative approach to channel equalization using a pattern classification strategy [3814-18]
Caimi, F. M. / Hassan, G. A. / SPIE et al. | 1999

Gedruckte Ausgabe
52: Results using an alternative approach to channel equalization using a pattern classification strategy
Caimi, Frank M. / Hassan, Gamal A. et al. | 1999

Elektronische Ausgabe
62: Method for JPEG standard progressive operation mode definition script construction and evaluation [3814-09]
Minguillon, J. / Pujol, J. / SPIE et al. | 1999

Gedruckte Ausgabe
62: Method for JPEG standard progressive operation mode definition script construction and evaluation
Minguillon, Julian / Pujol, Jaume et al. | 1999

Elektronische Ausgabe
73: EBLAST: efficient high-compression image transformation: I. Background and theory
Schmalz, Mark S. / Ritter, Gerhard X. / Caimi, Frank M. et al. | 1999

Elektronische Ausgabe
73: EBLAST: efficient high-compression image transformation: I. Background and theory [3814-10]
Schmalz, M. S. / Ritter, G. X. / Caimi, F. M. / SPIE et al. | 1999

Gedruckte Ausgabe
86: Trends in lossless image compression: adaptive vs. classified prediction and context modeling for entropy coding
Aiazzi, Bruno / Alparone, Luciano / Baronti, Stefano et al. | 1999

Elektronische Ausgabe
86: Trends in lossless image compression: adaptive vs. classified prediction and context modeling for entropy coding [3814-12]
Aiazzi, B. / Alparone, L. / Baronti, S. / SPIE et al. | 1999

Gedruckte Ausgabe
98: Mapping of image compression transforms to reconfigurable processors: simulation and analysis
Caimi, Frank M. / Schmalz, Mark S. / Ritter, Gerhard X. et al. | 1999

Elektronische Ausgabe
98: Mapping of image compression transforms to reconfigurable processors: simulation and analysis [3814-14]
Caimi, F. M. / Schmalz, M. S. / Ritter, G. X. / SPIE et al. | 1999

Gedruckte Ausgabe
115: Performance analysis of tabular nearest-neighbor encoding for joint image compression and ATR: I. Background and theory
Key, Gary / Schmalz, Mark S. / Caimi, Frank M. et al. | 1999

Elektronische Ausgabe
115: Performance analysis of tabular nearest-neighbor encoding for joint image compression and ATR: I. Background and theory [3814-15]
Key, G. / Schmalz, M. S. / Caimi, F. M. / SPIE et al. | 1999

Gedruckte Ausgabe
127: Performance analysis of tabular nearest-neighbor encoding for joint image compression and ATR: II. Results and analysis
Key, Gary / Schmalz, Mark S. / Caimi, Frank M. et al. | 1999

Elektronische Ausgabe
127: Performance analysis of tabular nearest-neighbor encoding for joint image compression and ATR: II. Results and analysis [3814-19]
Key, G. / Schmalz, M. S. / Caimi, F. M. / SPIE et al. | 1999

Gedruckte Ausgabe
143: MTF as a quality measure for compressed images transmitted over computer networks [3814-16]
Hadar, O. / Stern, A. / Huber, M. / Huber, R. / SPIE et al. | 1999

Gedruckte Ausgabe
143: MTF as a quality measure for compressed images transmitted over computer networks
Hadar, Ofer / Stern, Adrian / Huber, Merav / Huber, Revital et al. | 1999

Elektronische Ausgabe
155: Comparison of wavelet and Karhunen-Loeve transforms in video compression applications [3814-17]
Musatenko, Y. S. / Soloveyko, O. M. / Kurashov, V. N. / SPIE et al. | 1999

Gedruckte Ausgabe
155: Comparison of wavelet and Karhunen-Loeve transforms in video compression applications
Musatenko, Yurij S. / Soloveyko, Olexandr M. / Kurashov, Vitalij N. et al. | 1999

Elektronische Ausgabe

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Exportieren, teilen und zitieren

Performance analysis of tabular nearest-neighbor encoding for joint image compression and ATR: I. Background and theory [3814-15] (2024)

FAQs

How does nearest neighbor search work? ›

k-nearest neighbor search identifies the top k nearest neighbors to the query. This technique is commonly used in predictive analytics to estimate or classify a point based on the consensus of its neighbors. k-nearest neighbor graphs are graphs in which every point is connected to its k nearest neighbors.

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What is the approximate nearest neighbor algorithm? ›

An approximate nearest neighbor search algorithm is allowed to return points, whose distance from the query is at most c times the distance from the query to its nearest points. The appeal of this approach is that, in many cases, an approximate nearest neighbor is almost as good as the exact one.

View Details ›

Can k-NN be used for high dimensional data? ›

KNN's performance can degrade with very large datasets and high-dimensional spaces. It requires calculating distances for all instances in the training set, which can be computationally intensive.

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What is the nearest neighbors algorithm? ›

The k-nearest neighbors (KNN) algorithm is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point. It is one of the popular and simplest classification and regression classifiers used in machine learning today.

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What is the best algorithm for Neighbours search? ›

Popular ways to calculate nearest neighbor

K-nearest neighbors (KNN) The goal of KNN is usually to classify some piece of data against a large set of labeled data. ...
Approximate Nearest Neighbor (ANN) ...
Fixed radius nearest neighbor. ...
Partitioning with k-dimensional tree(k-d tree)

Jan 12, 2024

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How to interpret nearest neighbour analysis? ›

The scaling for identifying the distribution pattern is given below (Figure-2). If Rn value is close to 0 the distribution pattern is considered as clustered; if around 1.00 (. 5 to 1.5) it is Random;if Rn approaches towards 2.0, the pattern is uniform and if very close to 2.15 (the maximum) is perfectly Uniform.

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Why is nearest neighbor a lazy algorithm? ›

K-NN is a lazy learner because it doesn't learn a discriminative function from the training data but “memorizes” the training dataset instead. For example, the logistic regression algorithm learns its model weights (parameters) during training time. In contrast, there is no training time in K-NN.

Which is the efficient algorithm for nearest Neighbour classification? ›

The K-NN algorithm works by finding the K nearest neighbors to a given data point based on a distance metric, such as Euclidean distance. The class or value of the data point is then determined by the majority vote or average of the K neighbors.

Know More ›

What is the 1 nearest neighbor method? ›

The 1-N-N classifier is one of the oldest methods known. The idea is ex- tremely simple: to classify X find its closest neighbor among the training points (call it X ,) and assign to X the label of X .

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What is the curse of dimensionality in nearest neighbor? ›

Curse of dimensionality refers to a set of things that happen when you are dealing with items in high dimensional spaces, in particular what happens with distances and neighborhoods, in such a way that finding the nearest neighbors gets tricky. Consider a map of the world.

Read The Full Story ›

What is the curse of high dimensionality? ›

Curse of dimensionality also describes the phenomenon where the feature space becomes increasingly sparse for an increasing number of dimensions of a fixed-size training dataset. Intuitively, we can think of even the closest neighbors being too far away in a high-dimensional space to give a good estimate.

Read On ›

Can KNN handle large datasets? ›

The KNN algorithm does not work well with large datasets. The cost of calculating the distance between the new point and each existing point is huge, which degrades performance. Feature scaling (standardization and normalization) is required before applying the KNN algorithm to any dataset.

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What is the application of nearest Neighbour analysis? ›

The nearest neighbour analysis can be used to identify whether there are clustered land use zones within that section of the town. The buildings have been categorised by their primary use: Retail; Financial; Municipal; Private Other; Residential (Housing), and Green Space.

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What are the disadvantages of KNN? ›

The KNN algorithm has limitations in terms of scalability and the training process. It can be computationally expensive for large datasets, and the memory requirements can be significant. Additionally, KNN does not explicitly learn a model and assumes equal importance of all features.

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What is the complexity of the nearest neighbor algorithm? ›

The time complexity of the KNN algorithm for a single query point is O(nd), where n is the number of training examples and d is the number of features. This is because for each query point, the algorithm needs to compute the distance between the query point and every other point in the dataset.

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How does nearest neighbor matching work? ›

Nearest neighbor matching is also known as greedy matching. It involves running through the list of treated units and selecting the closest eligible control unit to be paired with each treated unit.

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How does nearest neighbor classifier works? ›

KNN classifier is a machine learning algorithm used for classification and regression problems. It works by finding the K nearest points in the training dataset and uses their class to predict the class or value of a new data point.

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How does the Annoy algorithm work? ›

The algorithm builds a binary search tree, where each node represents a hyperplane that splits the space into two subspaces. The tree is constructed in such a way that similar data points are likely to end up in the same subtree, making it faster to search for approximate nearest neighbors.

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