Scene Categorization Through Combining LBP and SIFT Features Effectively

2015 ◽  
Vol 30 (01) ◽  
pp. 1655001 ◽  
Author(s):  
Shung Bai ◽  
Jianjun Hou ◽  
Noboru Ohnishi
Keyword(s):  
Input Image ◽  
Support Vector ◽  
Two Dimensional ◽  
Scale Invariant ◽  
Scene Categorization ◽  
Visual Words ◽  
Training Images ◽  
Sift Features ◽  
Image Representations ◽  
Scale Invariant Feature

In computer vision, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT) are two widely used local descriptors. In this paper, we propose to combine them effectively for scene categorization. First, LBP and SIFT features are regularly extracted from training images for constructing a LBP feature codebook and a SIFT feature codebook. Then, a two-dimensional table is created by combining the obtained codebooks. For creating a representation for an image, LBP and SIFT features extracted from the same positions of the image are encoded together based on sparse coding by using the two-dimensional table. After processing all features in the input image, we adopt spatial max pooling to determine its representation. Obtained image representations are forwarded to a Support Vector Machine classifier for categorization. In addition, in order to improve the scene categorization performance further, we propose a method to select correlated visual words from large codebooks for constructing the two-dimensional table. Finally, for evaluating the proposed method, extensive experiments are implemented on datasets Scene Categories 8, Scene Categories 15 and MIT 67 Indoor Scene. It is demonstrated that the proposed method is effective for scene categorization.

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 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 An efficient method to classify GI tract images from WCE using visual words

2020 ◽  
Vol 10 (6) ◽  
pp. 5678
Author(s):  
R. Ponnusamy ◽  
S. Sathiamoorthy ◽  
R. Visalakshi
Keyword(s):  
Local Binary Pattern ◽  
Image Features ◽  
Support Vector ◽  
Gi Tract ◽  
Scale Invariant ◽  
Processing Scheme ◽  
Visual Words ◽  
Bag Of Features ◽  
Color Correlogram ◽  
Scale Invariant Feature

The digital images made with the Wireless Capsule Endoscopy (WCE) from the patient's gastrointestinal tract are used to forecast abnormalities. The big amount of information from WCE pictures could take 2 hours to review GI tract illnesses per patient to research the digestive system and evaluate them. It is highly time consuming and increases healthcare costs considerably. In order to overcome this problem, the CS-LBP (Center Symmetric Local Binary Pattern) and the ACC (Auto Color Correlogram) were proposed to use a novel method based on a visual bag of features (VBOF). In order to solve this issue, we suggested a Visual Bag of Features(VBOF) method by incorporating Scale Invariant Feature Transform (SIFT), Center-Symmetric Local Binary Pattern (CS-LBP) and Auto Color Correlogram (ACC). This combination of features is able to detect the interest point, texture and color information in an image. Features for each image are calculated to create a descriptor with a large dimension. The proposed feature descriptors are clustered by K- means referred to as visual words, and the Support Vector Machine (SVM) method is used to automatically classify multiple disease abnormalities from the GI tract. Finally, post-processing scheme is applied to deal with final classification results i.e. validated the performance of multi-abnormal disease frame detection.

scholarly journals Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 343 ◽  
Author(s):  
Jing Chen ◽  
Qi Liu ◽  
Lingwang Gao
Keyword(s):  
Plant Disease ◽  
Tea Plant ◽  
Plant Diseases ◽  
Support Vector ◽  
Scale Invariant ◽  
Visual Words ◽  
Svm Algorithm ◽  
Tea Plants ◽  
Scale Invariant Feature ◽  
Tea Leaf

The rapid, recent development of image recognition technologies has led to the widespread use of convolutional neural networks (CNNs) in automated image classification and in the recognition of plant diseases. Aims: The aim of the present study was to develop a deep CNNs to identify tea plant disease types from leaf images. Materials: A CNNs model named LeafNet was developed with different sized feature extractor filters that automatically extract the features of tea plant diseases from images. DSIFT (dense scale-invariant feature transform) features are also extracted and used to construct a bag of visual words (BOVW) model that is then used to classify diseases via support vector machine(SVM) and multi-layer perceptron(MLP) classifiers. The performance of the three classifiers in disease recognition were then individually evaluated. Results: The LeafNet algorithm identified tea leaf diseases most accurately, with an average classification accuracy of 90.16%, while that of the SVM algorithm was 60.62% and that of the MLP algorithm was 70.77%. Conclusions: The LeafNet was clearly superior in the recognition of tea leaf diseases compared to the MLP and SVM algorithms. Consequently, the LeafNet can be used in future applications to improve the efficiency and accuracy of disease diagnoses in tea plants.

PORNOGRAPHIC IMAGE DETECTION BASED ON MULTILEVEL REPRESENTATION

2009 ◽  
Vol 23 (08) ◽  
pp. 1633-1655 ◽  
Author(s):  
YUSHI WANG ◽  
QINGMING HUANG ◽  
WEN GAO
Keyword(s):  
Detection Method ◽  
Support Vector ◽  
Human Body Model ◽  
Scale Invariant ◽  
Visual Words ◽  
Detection Systems ◽  
Skin Region ◽  
Invariant Feature ◽  
Wide Range ◽  
Scale Invariant Feature

With the proliferation of pornographic images on the Internet, it is essential to automatically detect pornographic images by analyzing image content. Most traditional detection systems are based on low-level features and generate many false positives due to images that contain large regions of skin-like colors. In this paper, we present a novel detection method based on local features, such as SIFT (Scale Invariant Feature Transform) visual words. Support Vector Machine (SVM) is used to classify images according to their multilevel representation based on visual words and the distribution of pornography-related visual words. The multilevel representation captures inter-word statistics and fuses various visual components of pornographic scenes. Experimental results demonstrate that our method outperforms traditional skin-region and human-body-model based methods, and performs well on a wide range of test data, in particular, on human-related images.

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 Analysis of Application of Cluster Descriptions in Space of Characteristic Image Features

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 52 ◽  
Author(s):  
Oleksii Gorokhovatskyi ◽  
Volodymyr Gorokhovatskyi ◽  
Olena Peredrii
Keyword(s):  
Recognition Performance ◽  
Input Image ◽  
Image Features ◽  
Additional Time ◽  
Scale Invariant ◽  
Speeded Up Robust Features ◽  
Image Descriptors ◽  
Structural Image ◽  
Characteristic Features ◽  
Scale Invariant Feature

In this paper, we propose an investigation of the properties of structural image recognition methods in the cluster space of characteristic features. Recognition, which is based on key point descriptors like SIFT (Scale-invariant Feature Transform), SURF (Speeded Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), etc., often relating to the search for corresponding descriptor values between an input image and all etalon images, which require many operations and time. Recognition of the previously quantized (clustered) sets of descriptor features is described. Clustering is performed across the complete set of etalon image descriptors and followed by screening, which allows for representation of each etalon image in vector form as a distribution of clusters. Due to such representations, the number of computation and comparison procedures, which are the core of the recognition process, might be reduced tens of times. Respectively, the preprocessing stage takes additional time for clustering. The implementation of the proposed approach was tested on the Leeds Butterfly dataset. The dependence of cluster amount on recognition performance and processing time was investigated. It was proven that recognition may be performed up to nine times faster with only a moderate decrease in quality recognition compared to searching for correspondences between all existing descriptors in etalon images and input one without quantization.

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