scholarly journals Context-Dependent Object Proposal and Recognition

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1619
Author(s):  
Ray-I Chang ◽  
Chao-Lung Ting ◽  
Syuan-Yi Wu ◽  
Peng-Yeng Yin
Keyword(s):  
Object Recognition ◽  
Support Vector ◽  
Scale Invariant ◽  
Edge Image ◽  
Low Level ◽  
Automatic Driving ◽  
Object Proposal ◽  
Context Dependent ◽  
Scale Invariant Feature ◽  
Rule Out

Accurate and fast object recognition is crucial in applications such as automatic driving and unmanned aerial vehicles. Traditional object recognition methods relying on image-wise computations cannot afford such real-time applications. Object proposal methods appear to fit into this scenario by segmenting object-like regions to be further analyzed by sophisticated recognition models. Traditional object proposal methods have the drawback of generating many proposals in order to maintain a satisfactory recall of true objects. This paper presents two proposal refinement strategies based on low-level cues and context-dependent features, respectively. The low-level cues are used to enhance the edge image, while the context-dependent features are verified to rule out false objects that are irrelevant to our application. In particular, the context of the drink commodity is considered because the drink commodity has the largest sales in Taiwan’s convenience store chains, and the analysis of its context has great value in marketing and management. We further developed a support vector machine (SVM) based on the Bag of Words (BoW) model with scale-invariant feature transform (SIFT) descriptors to recognize the proposals. The experimental results show that our object proposal method generates many fewer proposals than those generated by Selective Search and EdgeBoxes, with similar recall. For the performance of SVM, at least 82% of drink objects are correctly recognized for test datasets of various challenging difficulties.

scholarly journals Efficient Technique for word identification and recognition in Telugu Documents

2019 ◽  
Vol 8 (2) ◽  
pp. 6053-6057
Keyword(s):  
Word Identification ◽  
Complex Structure ◽  
Support Vector ◽  
Indian Languages ◽  
Scale Invariant ◽  
Visual Words ◽  
Invariant Feature ◽  
Feature Transform ◽  
Word Images ◽  
Scale Invariant Feature

Telugu language is one of the most spoken Indian languages throughout the world. Since it has an old heritage, so Telugu literature and newspaper publications can be scanned to identify individual words. Identification of Telugu word images poses serious problems owing to its complex structure and larger set of individual characters. This paper aims to develop a novel methodology to achieve the same using SIFT (Scale Invariant Feature Transform) features of telugu words and classifying these features using BoVW (bag of visual words). The features are clustered to create a dictionary using k-means clustering. These words are used to create a visual codebook of the word images and the classification is achieved through SVM (Support Vector Machine).

scholarly journals SURF Points Versus SIFT Points in Identification of Medicinal Plants

2019 ◽  
Vol 9 (2) ◽  
pp. 602-607
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Medicinal Plants ◽  
Digital Image Processing ◽  
Digital Image ◽  
Support Vector ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

Today, digital image processing is used in diverse fields; this paper attempts to compare the outcome of two commonly used techniques namely Speeded Up Robust Feature (SURF) points and Scale Invariant Feature Transform (SIFT) points in image processing operations. This study focuses on leaf veins for identification of plants. An algorithm sequence has been utilized for the purpose of recognition of leaves. SURF and SIFT extractions are applied to define and distinguish the limited structures of the documented vein image of the leaf separately and Support Vector Machine (SVM) is integrated to classify and identify the correct plant. The results prove that the SURF algorithm is the fastest and an efficient one. The results of the study can be extrapolated to authenticate medicinal plants which is the starting step to standardize herbs and carryout research.

scholarly journals RESEARCH ON DIFFERENT FEATURE EXTRACTION AND MAMMOGRAM CLASSIFICATION TECHNIQUES

2019 ◽  
pp. 1-3
Author(s):  
Anita Kaklotar
Keyword(s):  
Feature Extraction ◽  
Association Rule ◽  
Support Vector ◽  
Common Disease ◽  
Extraction Techniques ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Mammogram Images

Breast cancer is the primary and the most common disease found among women. Today, mammography is the most powerful screening technique used for early detection of cancer which increases the chance of successful treatment. In order to correctly detect the mammogram images as being cancerous or malignant, there is a need of a classier. With this objective, an attempt is made to analyze different feature extraction techniques and classiers. In the proposed system we rst do the preprocessing of the mammogram images, where the unwanted noise and disturbances in the mammograms are removed. Features are then extracted from the mammogram images using Gray Level Co-Occurrences Matrix (GLCM) and Scale Invariant Feature Transform (SIFT). Finally, the features are classied using classiers like HiCARe (Classier based on High Condence Association Rule Agreements), Support Vector Machine (SVM), Naïve Bayes classier and K-NN Classier. Further we test the images and classify them as benign or malignant class.

scholarly journals THE LOW BACKSCATTERING OBJECTS CLASSIFICATION IN POLSAR IMAGE BASED ON BAG OF WORDS MODEL USING SUPPORT VECTOR MACHINE

2018 ◽  
Vol XLII-3 ◽  
pp. 2089-2091
Author(s):  
L. Yang ◽  
L. Shi ◽  
P. Li ◽  
J. Yang ◽  
L. Zhao ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Bare Soil ◽  
Radar Signal ◽  
Support Vector ◽  
Data Sets ◽  
Bag Of Words ◽  
Scale Invariant ◽  
Polarimetric Synthetic Aperture Radar ◽  
Sift Features ◽  
Scale Invariant Feature

Due to the forward scattering and block of radar signal, the water, bare soil, shadow, named low backscattering objects (LBOs), often present low backscattering intensity in polarimetric synthetic aperture radar (PolSAR) image. Because the LBOs rise similar backscattering intensity and polarimetric responses, the spectral-based classifiers are inefficient to deal with LBO classification, such as Wishart method. Although some polarimetric features had been exploited to relieve the confusion phenomenon, the backscattering features are still found unstable when the system noise floor varies in the range direction. This paper will introduce a simple but effective scene classification method based on Bag of Words (BoW) model using Support Vector Machine (SVM) to discriminate the LBOs, without relying on any polarimetric features. In the proposed approach, square windows are firstly opened around the LBOs adaptively to determine the scene images, and then the Scale-Invariant Feature Transform (SIFT) points are detected in training and test scenes. The several SIFT features detected are clustered using K-means to obtain certain cluster centers as the visual word lists and scene images are represented using word frequency. At last, the SVM is selected for training and predicting new scenes as some kind of LBOs. The proposed method is executed over two AIRSAR data sets at C band and L band, including water, bare soil and shadow scenes. The experimental results illustrate the effectiveness of the scene method in distinguishing LBOs.

scholarly journals Sparse coded spatial pyramid matching and multi-kernel integrated SVM for non-linear scene classification

2021 ◽  
Vol 72 (6) ◽  
pp. 374-380
Author(s):  
Bhavinkumar Gajjar ◽  
Hiren Mewada ◽  
Ashwin Patani
Keyword(s):  
Deep Learning ◽  
Support Vector ◽  
Scene Classification ◽  
Scale Invariant ◽  
Training Time ◽  
Multi Scale ◽  
Non Linear ◽  
Pyramid Matching ◽  
Scale Invariant Feature ◽  
Distinct Features

Abstract Support vector machine (SVM) techniques and deep learning have been prevalent in object classification for many years. However, deep learning is computation-intensive and can require a long training time. SVM is significantly faster than Convolution Neural Network (CNN). However, the SVM has limited its applications in the mid-size dataset as it requires proper tuning. Recently the parameterization of multiple kernels has shown greater flexibility in the characterization of the dataset. Therefore, this paper proposes a sparse coded multi-scale approach to reduce training complexity and tuning of SVM using a non-linear fusion of kernels for large class natural scene classification. The optimum features are obtained by parameterizing the dictionary, Scale Invariant Feature Transform (SIFT) parameters, and fusion of multiple kernels. Experiments were conducted on a large dataset to examine the multi-kernel space capability to find distinct features for better classification. The proposed approach founds to be promising than the linear multi-kernel SVM approaches achieving 91.12 % maximum accuracy.

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