CMLBPIncoherent: a New Contextual Image Descriptor for Scene Classification (original) (raw)
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Objects classification or object detection is one of the most challenging tasks in computer vision. Digital images taken of real-life scenes capture objects at different positions, rotations and scales. Furthermore, variations in lighting, shape, color and texture within the same class make object classification a huge obstacle for computer vision algorithms. The most robust methodologies related to variations in lighting, rotation, color and scale, are based on "key points" localization, followed by applying a local descriptor to each surrounding region. Researchers have used various methods for detecting key points and have applied various local descriptors. Of these, the Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF) and Center-Symmetric Local Binary Pattern (CS-LBP) methods have obtained good performance and are associated with clustering algorithms or histogram representation based on independent features (Bag of Features (BoF)). In the BoF approach, the visual codebook extracted around the "key points" regions can effectively describe objects by their appearance based on local texture analysis. Recently, we proposed two new texture descriptors for object detection based on the Local Mapped Pattern (LMP) approach. The Mean-Local Mapped Pattern (M-LMP) and the Center Symmetric Local Mapped Pattern (CS-LMP) exhibit better performance than SIFT and CS-LBP, but prior results have shown that the size of descriptors could be reduced without loss of sensitivity. In this paper, we investigated reducing the size of the M-LMP descriptor and then evaluating its performance for object classification by a Support Vector Machine (SVM) classifier. In our experiments, we implemented an object recognition system based on the M-LMP reduced descriptor, and compared our results against the SIFT, Local Intensity Order Pattern (LIOP) and CS-LMP descriptors. The object classification results were analyzed using a BoF model and a SVM classifier, with the result that performance using Adilson Gonzaga
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This paper presents novel color, texture and shape descriptors for scene and object image classification and evaluates their performance in unconventional color spaces. First, a new three dimensional Local Binary Pattern (3D-LBP) descriptor is proposed for color and texture feature extraction. Second, a novel color HWML (HOG of Wavelet of Multiplanar LBP) descriptor is derived by computing the histogram of the orientation gradients (HOG) of the Haar wavelet transformation of the original image and the 3D-LBP images. Third, these descriptors are generated in the unconventional color spaces like oRGB, I1I2I3, uncorrelated and discriminating color spaces to improve performance over conventional color spaces like RGB and HSV. Fourth, the Enhanced Fisher Model (EFM) is applied for discriminatory feature extraction and the nearest neighbor classification rule is used for image classification. Finally, the Caltech 256 object categories database and the MIT scene dataset are used to demonst...
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Four novel color Local Binary Pattern (LBP) descriptors are presented in this chapter for scene image and image texture classification with applications to image search and retrieval. Specifically, the first color LBP descriptor, the oRGB-LBP descriptor, is derived by concatenating the LBP features of the component images in an opponent color space — the oRGB color space. The other three color LBP descriptors are obtained by the integration of the oRGB-LBP descriptor with some additional image features: the Color LBP Fusion (CLF) descriptor is constructed by integrating the RGB-LBP, the YCbCr-LBP, the HSV-LBP, the rgb-LBP, as well as the oRGB-LBP descriptor; the Color Grayscale LBP Fusion (CGLF) descriptor is derived by integrating the grayscale-LBP descriptor and the CLF descriptor; and the CGLF+PHOG descriptor is obtained by integrating the Pyramid of Histograms of Orientation Gradients (PHOG) and the CGLF descriptor. Feature extraction applies the Enhanced Fisher Model (EFM) and ...
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2014 International Joint Conference on Neural Networks (IJCNN), 2014
This paper introduces a new local feature description method to categorize scene images. We encode local image information by exploring the pseudo-Wigner distribution of images and the Local Binary Patterns (LBP) technique and make four major contributions. In particular, we first define a multi-neighborhood LBP for small image blocks. Second, we combine the multi-neighborhood LBP with the pseudo-Wigner distribution of images for feature extraction. Third, we derive the innovative WLBP feature vector by utilizing the frequency domain smoothing, the bag-of-words model and spatial pyramid representations of an image. Finally, we perform extensive experiments to evaluate the performance of the proposed WLBP descriptor. Specifically, we test our descriptor for classification performance using a Support Vector Machine (SVM) classifier on three fairly challenging publicly available image datasets, namely the UIUC Sports Event dataset, the Fifteen Scene Categories dataset and the MIT Scene dataset. Experimental results reveal that the proposed WLBP descriptor outperforms the traditional LBP technique and yields results better than some other popular image descriptors.
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2010 20th International Conference on Pattern Recognition, 2010
The Local Binary Pattern (LBP) operator is a computationally efficient yet powerful feature for analyzing local texture structures. While the LBP operator has been successfully applied to tasks as diverse as texture classification, texture segmentation, face recognition and facial expression recognition, etc., it has been rarely used in the domain of Visual Object Classes (VOC) recognition mainly due to its deficiency of power for dealing with various changes in lighting and viewing conditions in real-world scenes. In this paper, we propose six novel multi-scale color LBP operators in order to increase photometric invariance property and discriminative power of the original LBP operator. The experimental results on the PASCAL VOC 2007 image benchmark show significant accuracy improvement by the proposed operators as compared with both the original LBP and other popular texture descriptors such as Gabor filter.