Atreyee Sinha

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This paper presents new image descriptors based on color, texture, shape, and wavelets for object and scene image classification. First, a new three Dimensional Local Binary Patterns (3D-LBP) descriptor, which produces three new color images, is proposed for encoding both color and texture information of an image. The 3D-LBP images together with the(More)
The Histograms of Oriented Gradients (HOG) descriptor represents shape information by storing the local gradients in an image. The Haar wavelet transform is a simple yet powerful technique that can separately enhance the horizontal and vertical local features in an image. In this paper, we enhance the HOG descriptor by subjecting the image to the Haar(More)
1,574 images from eight sports event categories. The MIT Scene dataset (also known as OT Scenes) [6] has 2,688 images classified as eight categories. The Fifteen Scene Categories dataset [7] is composed of 15 scene categories with 200 to 400 images in each. Figure 3 shows sample images from each dataset. Figures 4 and 5 show the improvement achieved by BoWL(More)
This paper presents a novel set of image descriptors that encodes information from color, shape, spatial and local features of an image to improve upon the popular Pyramid of Histograms of Oriented Gradients (PHOG) descriptor for object and scene image classification. In particular, a new Gabor-PHOG (GPHOG) image descriptor created by enhancing the local(More)
This paper presents a novel set of color descriptors for object and scene image classification. We first introduce a new Gabor-PHOG (GPHOG) descriptor by concatenating the Pyramid of Histograms of Oriented Gradients (PHOG) of the local Gabor filtered images. Second, we derive the Gabor-LBP (GLBP) descriptor by accumulating the Local Binary Patterns (LBP)(More)
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(More)
Several new image descriptors are presented in this paper that combine color, texture and shape information to create feature vectors for scene and object image classification. In particular, first, a new three dimensional Local Binary Patterns (3D-LBP) descriptor is proposed for color image local feature extraction. Second, three novel color HWML (HOG of(More)
This paper presents several novel Gabor-based color descriptors for object and scene image classification. Firstly, a new Gabor-HOG descriptor is proposed for image feature extraction. Secondly, the Gabor-LBP descriptor derived by concatenating the Local Binary Patterns (LBP) histograms of all the component images produced by applying Gabor filters is(More)
This paper introduces several novel color, shape and texture-based image descriptors for scene image classification with applications to image search and retrieval. Specifically, first, a new 3-Dimensional Local Binary Pattern (3DLBP) descriptor is proposed for color image local feature extraction. Second, a new shape descriptor (HaarHOG) is introduced by(More)