Low-Level Features for Image Retrieval Based on Extraction of Directional Binary Patterns and Its Oriented Gradients Histogram

In this paper, we present a novel approach for image retrieval based on extraction of low level features using techniques such as Directional Binary Code, Haar Wavelet transform and Histogram of Oriented Gradients. The DBC texture descriptor captures the spatial relationship between any pair of neighbourhood pixels in a local region along a given direction, while Local Binary Patterns descriptor considers the relationship between a given pixel and its surrounding neighbours. Therefore, DBC captures more spatial information than LBP and its variants, also it can extract more edge information than LBP. Hence, we employ DBC technique in order to extract grey level texture feature from each RGB channels individually and computed texture maps are further combined which represents colour texture features of an image. Then, we decomposed the extracted colour texture map and original image using Haar wavelet transform. Finally, we encode the shape and local features of wavelet transformed images using Histogram of Oriented Gradients for content based image retrieval. The performance of proposed method is compared with existing methods on two databases such as Wang's corel image and Caltech 256. The evaluation results show that our approach outperforms the existing methods for image retrieval.Comment: 7 Figures, 5 Tables 16 Pages in Computer Applications: An International Journal (CAIJ), Vol.2, No.1, February 201

Global spectral graph wavelet signature for surface analysis of carpal bones

In this paper, we present a spectral graph wavelet approach for shape analysis of carpal bones of human wrist. We apply a metric called global spectral graph wavelet signature for representation of cortical surface of the carpal bone based on eigensystem of Laplace-Beltrami operator. Furthermore, we propose a heuristic and efficient way of aggregating local descriptors of a carpal bone surface to global descriptor. The resultant global descriptor is not only isometric invariant, but also much more efficient and requires less memory storage. We perform experiments on shape of the carpal bones of ten women and ten men from a publicly-available database. Experimental results show the excellency of the proposed GSGW compared to recent proposed GPS embedding approach for comparing shapes of the carpal bones across populations.Comment: arXiv admin note: substantial text overlap with arXiv:1705.0625

Shape Classification using Spectral Graph Wavelets

Spectral shape descriptors have been used extensively in a broad spectrum of geometry processing applications ranging from shape retrieval and segmentation to classification. In this pa- per, we propose a spectral graph wavelet approach for 3D shape classification using the bag-of-features paradigm. In an effort to capture both the local and global geometry of a 3D shape, we present a three-step feature description framework. First, local descriptors are extracted via the spectral graph wavelet transform having the Mexican hat wavelet as a generating ker- nel. Second, mid-level features are obtained by embedding lo- cal descriptors into the visual vocabulary space using the soft- assignment coding step of the bag-of-features model. Third, a global descriptor is constructed by aggregating mid-level fea- tures weighted by a geodesic exponential kernel, resulting in a matrix representation that describes the frequency of appearance of nearby codewords in the vocabulary. Experimental results on two standard 3D shape benchmarks demonstrate the effective- ness of the proposed classification approach in comparison with state-of-the-art methods

Local Feature Detectors, Descriptors, and Image Representations: A Survey

With the advances in both stable interest region detectors and robust and distinctive descriptors, local feature-based image or object retrieval has become a popular research topic. %All of the local feature-based image retrieval system involves two important processes: local feature extraction and image representation. The other key technology for image retrieval systems is image representation such as the bag-of-visual words (BoVW), Fisher vector, or Vector of Locally Aggregated Descriptors (VLAD) framework. In this paper, we review local features and image representations for image retrieval. Because many and many methods are proposed in this area, these methods are grouped into several classes and summarized. In addition, recent deep learning-based approaches for image retrieval are briefly reviewed.Comment: 20 page

Point Context: An Effective Shape Descriptor for RST-invariant Trajectory Recognition

Motion trajectory recognition is important for characterizing the moving property of an object. The speed and accuracy of trajectory recognition rely on a compact and discriminative feature representation, and the situations of varying rotation, scaling and translation has to be specially considered. In this paper we propose a novel feature extraction method for trajectories. Firstly a trajectory is represented by a proposed point context, which is a rotation-scale-translation (RST) invariant shape descriptor with a flexible tradeoff between computational complexity and discrimination, yet we prove that it is a complete shape descriptor. Secondly, the shape context is nonlinearly mapped to a subspace by kernel nonparametric discriminant analysis (KNDA) to get a compact feature representation, and thus a trajectory is projected to a single point in a low-dimensional feature space. Experimental results show that, the proposed trajectory feature shows encouraging improvement than state-of-art methods.Comment: 11 pages, 10 figure

A Fusion of Labeled-Grid Shape Descriptors with Weighted Ranking Algorithm for Shapes Recognition

Retrieving similar images from a large dataset based on the image content has been a very active research area and is a very challenging task. Studies have shown that retrieving similar images based on their shape is a very effective method. For this purpose a large number of methods exist in literature. The combination of more than one feature has also been investigated for this purpose and has shown promising results. In this paper a fusion based shapes recognition method has been proposed. A set of local boundary based and region based features are derived from the labeled grid based representation of the shape and are combined with a few global shape features to produce a composite shape descriptor. This composite shape descriptor is then used in a weighted ranking algorithm to find similarities among shapes from a large dataset. The experimental analysis has shown that the proposed method is powerful enough to discriminate the geometrically similar shapes from the non-similar ones

Content-Based Video Browsing by Text Region Localization and Classification

The amount of digital video data is increasing over the world. It highlights the need for efficient algorithms that can index, retrieve and browse this data by content. This can be achieved by identifying semantic description captured automatically from video structure. Among these descriptions, text within video is considered as rich features that enable a good way for video indexing and browsing. Unlike most video text detection and extraction methods that treat video sequences as collections of still images, we propose in this paper spatiotemporal. video-text localization and identification approach which proceeds in two main steps: text region localization and text region classification. In the first step we detect the significant appearance of the new objects in a frame by a split and merge processes applied on binarized edge frame pair differences. Detected objects are, a priori, considered as text. They are then filtered according to both local contrast variation and texture criteria in order to get the effective ones. The resulted text regions are classified based on a visual grammar descriptor containing a set of semantic text class regions characterized by visual features. A visual table of content is then generated based on extracted text regions occurring within video sequence enriched by a semantic identification. The experimentation performed on a variety of video sequences shows the efficiency of our approach.Comment: 11 pages, 12 figures, International Journal of Video & Image Processing and Network Security IJVIPNS-IJENS Vol:10 No: 0

Survey of Computer Vision and Machine Learning in Gastrointestinal Endoscopy

This paper attempts to provide the reader a place to begin studying the application of computer vision and machine learning to gastrointestinal (GI) endoscopy. They have been classified into 18 categories. It should be be noted by the reader that this is a review from pre-deep learning era. A lot of deep learning based applications have not been covered in this thesis

Deep Learning Representation using Autoencoder for 3D Shape Retrieval

We study the problem of how to build a deep learning representation for 3D shape. Deep learning has shown to be very effective in variety of visual applications, such as image classification and object detection. However, it has not been successfully applied to 3D shape recognition. This is because 3D shape has complex structure in 3D space and there are limited number of 3D shapes for feature learning. To address these problems, we project 3D shapes into 2D space and use autoencoder for feature learning on the 2D images. High accuracy 3D shape retrieval performance is obtained by aggregating the features learned on 2D images. In addition, we show the proposed deep learning feature is complementary to conventional local image descriptors. By combing the global deep learning representation and the local descriptor representation, our method can obtain the state-of-the-art performance on 3D shape retrieval benchmarks.Comment: 6 pages, 7 figures, 2014ICSPA

Four Side Distance: A New Fourier Shape Signature

Shape is one of the main features in content based image retrieval (CBIR). This paper proposes a new shape signature. In this technique, features of each shape are extracted based on four sides of the rectangle that covers the shape. The proposed technique is Fourier based and it is invariant to translation, scaling and rotation. The retrieval performance between some commonly used Fourier based signatures and the proposed four sides distance (FSD) signature has been tested using MPEG-7 database. Experimental results are shown that the FSD signature has better performance compared with those signatures.Comment: 6 pages, 7 figures, International Journal of Advanced Studies in Computers, Science and Engineerin