An Object Classification Approach Based on Randomized Visual Vocabulary and Clustering Aggregation

2013 ◽  
Vol 433-435 ◽  
pp. 778-782 ◽  
Author(s):  
Li Su ◽  
Jie Nan Liu ◽  
Lan Fang Ren ◽  
Feng Zhang
Keyword(s):  
Object Classification ◽  
Local Features ◽  
Locality Sensitive Hashing ◽  
Training Dataset ◽  
Support Vector ◽  
Visual Words ◽  
Visual Vocabulary ◽  
Vector Machines ◽  
Clustering Aggregation ◽  
Aggregation Technique

Considering the problems with the conventional Bag-of-Visual-Words approaches, such as high time consumption, the synonymy and ambiguity of visual word, and instability of clustering high-dimensionality image local features, this paper presents a novel object classificaiton approach based on randomized visual vocabulary and clustering aggregation. Firstly, Exact Euclidean Locality Sensitive Hashing (E2LSH) is used to cluster local features of the training dataset, and a group of randomized visual vocabularies is constructed. Then, the randomized visual vocabularies are aggregated using clustering aggregation technique, resulting in Randomized Visual Vocabularies Aggregating Dictionary (RVVAD). Finally, the visual words histogram is generated according to the dictionary, and the Support Vector Machines are learned to accomplish image object categorization. Experimental results indicate that the expression ability of the dictionary is effectively improved, and the object classification precision is increased dramatically.

A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine

2018 ◽  
Vol 45 (1) ◽  
pp. 117-135 ◽  
Author(s):  
Amna Sarwar ◽  
Zahid Mehmood ◽  
Tanzila Saba ◽  
Khurram Ashfaq Qazi ◽  
Ahmed Adnan ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Image Databases ◽  
Semantic Gap ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Bag Of Words ◽  
Visual Words ◽  
Visual Attributes ◽  
Visual Vocabulary ◽  
Novel Method

The advancements in the multimedia technologies result in the growth of the image databases. To retrieve images from such image databases using visual attributes of the images is a challenging task due to the close visual appearance among the visual attributes of these images, which also introduces the issue of the semantic gap. In this article, we recommend a novel method established on the bag-of-words (BoW) model, which perform visual words integration of the local intensity order pattern (LIOP) feature and local binary pattern variance (LBPV) feature to reduce the issue of the semantic gap and enhance the performance of the content-based image retrieval (CBIR). The recommended method uses LIOP and LBPV features to build two smaller size visual vocabularies (one from each feature), which are integrated together to build a larger size of the visual vocabulary, which also contains complementary features of both descriptors. Because for efficient CBIR, the smaller size of the visual vocabulary improves the recall, while the bigger size of the visual vocabulary improves the precision or accuracy of the CBIR. The comparative analysis of the recommended method is performed on three image databases, namely, WANG-1K, WANG-1.5K and Holidays. The experimental analysis of the recommended method on these image databases proves its robust performance as compared with the recent CBIR methods.

Online time-sequence incremental and decremental least squares support vector machines for engine air-ratio prediction

2011 ◽  
Vol 13 (1) ◽  
pp. 28-40 ◽  
Author(s):  
P-K Wong ◽  
H-C Wong ◽  
C-M Vong
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Fuel Efficiency ◽  
Time Sequence ◽  
Operating Conditions ◽  
Incremental Algorithm ◽  
Training Dataset ◽  
Support Vector ◽  
Fuel Ratio ◽  
Vector Machines

Fuel efficiency and pollution reduction relate closely to air-ratio (i.e. lambda) control among all the engine control variables. Lambda indicates the amount that the actual available air-fuel ratio mixture differs from the stoichiometric air-fuel ratio of the fuel being used. Accurate lambda prediction is essential for effective lambda control. This paper employs an emerging online time-sequence incremental algorithm and proposes one novel online time-sequence decremental algorithm based on least squares support vector machines (LS-SVMs) to continually update the built LS-SVM lambda function whenever a sample is added to, or removed from, the training dataset. Moreover, the online time-sequence algorithm can also significantly shorten the function updating time as compared with function retraining from scratch. In order to evaluate the effectiveness of this pair of online time-sequence algorithms, three lambda time series obtained from experiments under different operating conditions are employed. The prediction results of the online time-sequence algorithms over unseen cases are compared with those under classical LS-SVMs, typical decremental LS-SVMs, and neural networks. Experimental results show that the online time-sequence incremental and decremental LS-SVMs are superior to the other three typical methods.

Support Vector Machines and Applications

2018 ◽  
pp. 1381-1390
Author(s):  
Vandana M. Ladwani
Keyword(s):  
Support Vector Machines ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Decision Boundary ◽  
Visual Words ◽  
Vector Machines ◽  
Special Subset ◽  
Multiclass Svm ◽  
Unique Decision

Support Vector Machines is one of the powerful Machine learning algorithms used for numerous applications. Support Vector Machines generate decision boundary between two classes which is characterized by special subset of the training data called as Support Vectors. The advantage of support vector machine over perceptron is that it generates a unique decision boundary with maximum margin. Kernalized version makes it very faster to learn as the data transformation is implicit. Object recognition using multiclass SVM is discussed in the chapter. The experiment uses histogram of visual words and multiclass SVM for image classification.

Support Vector Machines and Applications

2017 ◽  
pp. 254-262
Author(s):  
Vandana M. Ladwani
Keyword(s):  
Support Vector Machines ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Decision Boundary ◽  
Visual Words ◽  
Vector Machines ◽  
Special Subset ◽  
Multiclass Svm ◽  
Unique Decision

Support Vector Machines is one of the powerful Machine learning algorithms used for numerous applications. Support Vector Machines generate decision boundary between two classes which is characterized by special subset of the training data called as Support Vectors. The advantage of support vector machine over perceptron is that it generates a unique decision boundary with maximum margin. Kernalized version makes it very faster to learn as the data transformation is implicit. Object recognition using multiclass SVM is discussed in the chapter. The experiment uses histogram of visual words and multiclass SVM for image classification.

Smartphone Naïve Bayes Human Activity Recognition Using Personalized Datasets

2020 ◽  
Vol 24 (5) ◽  
pp. 685-702
Author(s):  
Moses L. Gadebe ◽  
◽  
Okuthe P. Kogeda ◽  
Sunday O. Ojo
Keyword(s):  
Support Vector Machines ◽  
Real Time ◽  
Activity Recognition ◽  
Human Activity ◽  
Nearest Neighbor ◽  
Human Activity Recognition ◽  
Training Dataset ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Vector Machines

Recognizing human activity in real time with a limited dataset is possible on a resource-constrained device. However, most classification algorithms such as Support Vector Machines, C4.5, and K Nearest Neighbor require a large dataset to accurately predict human activities. In this paper, we present a novel real-time human activity recognition model based on Gaussian Naïve Bayes (GNB) algorithm using a personalized JavaScript object notation dataset extracted from the publicly available Physical Activity Monitoring for Aging People dataset and University of Southern California Human Activity dataset. With the proposed method, the personalized JSON training dataset is extracted and compressed into a 12×8 multi-dimensional array of the time-domain features extracted using a signal magnitude vector and tilt angles from tri-axial accelerometer sensor data. The algorithm is implemented on the Android platform using the Cordova cross-platform framework with HTML5 and JavaScript. Leave-one-activity-out cross validation is implemented as a testTrainer() function, the results of which are presented using a confusion matrix. The testTrainer() function leaves category K as the testing subset and the remaining K-1 as the training dataset to validate the proposed GNB algorithm. The proposed model is inexpensive in terms of memory and computational power owing to the use of a compressed small training dataset. Each K category was repeated five times and the algorithm consistently produced the same result for each test. The result of the simulation using the tilted angle features shows overall precision, recall, F-measure, and accuracy rates of 90%, 99.6%, 94.18%, and 89.51% respectively, in comparison to rates of 36.9%, 75%, 42%, and 36.9% when the signal magnitude vector features were used. The results of the simulations confirmed and proved that when using the tilt angle dataset, the GNB algorithm is superior to Support Vector Machines, C4.5, and K Nearest Neighbor algorithms.

Mastitis detection in dairy cows: the application of support vector machines

2013 ◽  
Vol 151 (6) ◽  
pp. 889-897 ◽  
Author(s):  
B. MIEKLEY ◽  
I. TRAULSEN ◽  
J. KRIETER
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machines ◽  
Moving Average ◽  
Biological Data ◽  
Training Dataset ◽  
Sequence Length ◽  
Support Vector ◽  
Stage Of Lactation ◽  
Vector Machines ◽  
Indicator Variables

SUMMARYThe current investigation analysed the applicability of support vector machines (SVMs), a sub-discipline in the field of artificial intelligence, for the early detection of mastitis. Data used were recorded on the Karkendamm dairy research farm (Kiel, Germany) between January 2010 and December 2011. Data from 215 cows in their first 200 days in milk (DIM) were analysed. Mastitis was specified according to veterinary treatments and defined as disease blocks. The two different definitions used varied solely in the sequence length of the blocks. Only the days before the treatment were included in the blocks. The following parameters were used for the recognition of mastitis: milk electrical conductivity (MEC), milk yield (MY), stage of lactation, month, mastitis history during lactation, deviation from the 5-day moving average of MEC as well as MY, and the 5-day moving standard deviations of the same traits. To develop and verify the model of the SVMs, the mastitis dataset was divided into training and test datasets. Support vector machines are tools for statistical pattern recognition, focusing on algorithms capable of learning and adapting the structure of the input parameters based on the training dataset. The results show that the block sensitivity of mastitis detection considering both mastitis definitions was 84·6%, while specificity was 71·6 and 78·3%, respectively. Showing feasible features for pattern recognition of biological data, SVMs can principally be applied for disease detection. However, without further performance improvement or different study settings (e.g. other indicator variables) SVMs cannot be easily implemented into practical usage.

scholarly journals A CONTENT DISCLOSURE AND TEXT IDENTIFICATION BASED ON ROAD-LEVEL SYMBOLISM USING TCH ALGORITHM

2015 ◽  
Vol 3 (2) ◽  
pp. 38-46
Author(s):  
Ramya R ◽  
Vinothini K. R ◽  
Manikandan
Keyword(s):  
Language Model ◽  
Geographical Area ◽  
Text Recognition ◽  
Support Vector ◽  
Traffic Sign ◽  
Visual Appearance ◽  
Visual Words ◽  
Vector Machines ◽  
Enrichment Techniques

Image enhancement is a process of improving the quality of image by improving its feature. Image contrast enrichment techniques have been largely studied in the past decades. Traffic sign disclosure and realization has been fully prepared for a great past. Traffic board detection and text recognition still leaving a protest in computer view due to its various group and the vast changeability   of the data illustrated in them. The essential function can be to make an automated index of the traffic panels placed in a road to holding its care and to aid drivers. Then the figure are defined as a “bag of visual words” and restricted to applying support vector machines. Completely our own text detection and recognition method is tested on those images where a traffic board has been disclosure in system to naturally read and save the data details in the panels. Leading driver aid scheme could also use from text recognition for automated traffic signs and panels description. This visual appearance categorization method is a new approach for traffic panel detection in the state of the art. We propose a language model partly based on a dynamic dictionary for a limited geographical area using a reverse geocoding service. Experimental results on real images from Google Street View prove the efficiency of the proposed method and give way to using street-level images for different applications on ITS.

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