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

    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

    • Schlagwörter:

      mathematics , data/image coding , compression

    • Datenquelle:

      British Library Conference Proceedings

    © Metadata Copyright the British Library Board and other contributors. All rights reserved.

    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 [3814-02]

    Miyamoto, M. / Tanaka, K. / Sugimura, T. / 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

    26

    Information hiding using random sequences

    Kim, Jang-Hwan / Kim, Kyu-Tae / Kim, Eun-Soo et al. | 1999

    Elektronische Ausgabe

    26

    Information hiding using random sequences [3814-04]

    Kim, J.-H. / Kim, K.-T. / Kim, E.-S. / SPIE et al. | 1999

    Gedruckte 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 [3814-07]

    Abrardo, A. / Barni, M. / Garzelli, A. / SPIE et al. | 1999

    Gedruckte Ausgabe

    43

    Unequal error protection for H.263 video over indoor DECT channel

    Abrardo, Andrea / Barni, Mauro / Garzelli, Andrea et al. | 1999

    Elektronische 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

    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

    62

    Method for JPEG standard progressive operation mode definition script construction and evaluation

    Minguillon, Julian / Pujol, Jaume 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

    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 [3814-12]

    Aiazzi, B. / Alparone, L. / Baronti, S. / 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

    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

    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

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

    FAQs

    How to choose the value of k in KNN? ›

    The choice of k will largely depend on the input data as data with more outliers or noise will likely perform better with higher values of k. Overall, it is recommended to have an odd number for k to avoid ties in classification, and cross-validation tactics can help you choose the optimal k for your dataset.

    What is the formula for K nearest neighbor? ›

    The k-nearest neighbor classifier fundamentally relies on a distance metric. The better that metric reflects label similarity, the better the classified will be. The most common choice is the Minkowski distance dist(x,z)=(d∑r=1|xr−zr|p)1/p.

    What is the algorithm for K nearest neighbors? ›

    How Does the K-Nearest Neighbors Algorithm Work?
    • Step #1 - Assign a value to K.
    • Step #2 - Calculate the distance between the new data entry and all other existing data entries (you'll learn how to do this shortly). ...
    • Step #3 - Find the K nearest neighbors to the new entry based on the calculated distances.
    Jan 25, 2023

    What are the metrics of KNN evaluation? ›

    The choice of performance metric largely depends on the problem that KNN is trying to solve. Some common classification metrics include accuracy, precision, recall, f1 score, ROC AUC, and PR AUC. Common regression metrics include MSE, RMSE, MAE, and R2. The right metric to choose also depends heavily on the dataset.

    What does a high K value mean in KNN? ›

    The value of k in the KNN algorithm is related to the error rate of the model. A small value of k could lead to overfitting as well as a big value of k can lead to underfitting. Overfitting imply that the model is well on the training data but has poor performance when new data is coming.

    What is optimal K for KNN regression? ›

    The optimal K value usually found is the square root of N, where N is the total number of samples. Use an error plot or accuracy plot to find the most favorable K value. KNN performs well with multi-label classes, but you must be aware of the outliers.

    How to manually calculate KNN? ›

    KNN Algorithm Manual Implementation
    1. Step1: Calculate the Euclidean distance between the new point and the existing points. ...
    2. Step 2: Choose the value of K and select K neighbor's closet to the new point. ...
    3. Step 3: Count the votes of all the K neighbors / Predicting Values. ...
    4. Step 1: Handling the data.
    Jul 26, 2018

    What is KNN for beginners? ›

    The K-Nearest Neighbors (KNN) algorithm is a popular machine learning technique used for classification and regression tasks. It relies on the idea that similar data points tend to have similar labels or values. During the training phase, the KNN algorithm stores the entire training dataset as a reference.

    How do I choose my nearest neighbor K? ›

    You can use the common formula k = sqrt(n) where n is the number of data points in your training set or you can try choosing k where there is a good balance between computation expense vs noise.

    What is k in the k nearest neighbors algorithm? ›

    It then assigns the most common class label (among those k training examples) to the test example. k is therefore just the number of neighbors "voting" on the test example's class. If k=1, then test examples are given the same label as the closest example in the training set.

    Is K nearest neighbor a lazy algorithm? ›

    K-NN is a non-parametric algorithm, which means that it does not make any assumptions about the underlying data. It is also called a lazy learner algorithm because it does not learn from the training set immediately instead it stores the data set and at the time of classification it performs an action on the data set.

    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.

    How can I improve my KNN results? ›

    What are the most effective ways to improve k-nearest neighbor search accuracy?
    1. Choose the right k value.
    2. Use a suitable distance metric.
    3. Scale and normalize the data. Be the first to add your personal experience.
    4. Reduce the dimensionality. ...
    5. Use an efficient data structure. ...
    6. Use an ensemble method. ...
    7. Here's what else to consider.
    Dec 28, 2023

    What is a good accuracy for KNN? ›

    A recent study by [11] found that the kNN method gave a best result of 48.78% with k = 8 when applied on a dataset which has 395 records, 30 attributes, and 4 classes. The relatively low accuracy of kNN is caused by several factors.

    What is the best value for KNN? ›

    Square Root of N rule: This rule offers a quick and practical way to determine an initial k value for your KNN model, especially when no other domain-specific knowledge or optimization techniques are readily available. The rule suggests setting k to the square root of N.

    How to choose the k value? ›

    The Elbow Method

    Calculate the Within-Cluster-Sum of Squared Errors (WSS) for different values of k, and choose the k for which WSS becomes first starts to diminish. In the plot of WSS-versus-k, this is visible as an elbow.

    How would you choose the value of k? ›

    There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster.

    How do we choose the factor k? ›

    How to Choose the Factor 'K'? A KNN algorithm is based on feature similarity. Selecting the right K value is a process called parameter tuning, which is important to achieve higher accuracy. There is not a definitive way to determine the best value of K.

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