Combination Of Local Descriptors Research Articles

An IoT based authentication system for therapeutic herbs measured by local descriptors using machine learning approach

The work aims to develop an automatic recognition model to classify medicinal plants using machine learning techniques to enrich the traditional medical system of India. Though many countries have accepted conventional medicine as the best alternative to synthetic drugs, there exists limitations such as lack of awareness among general public and unavailability of easy access to its source evidences that has led to its limited acceptance and usability. Herein, an intelligent system is proposed to use Raspberry Pi 3 Model B+ (RPi) and the RPi camera to capture the leaf images of Indian medicinal herbs and reveal their medical properties. Five types of models implemented to identify the medicinal plants. One of the models proposed as Herbmodel extracts a feature map from a captured medicinal leaf by combining three different feature extraction techniques, namely, Scale Invariant Feature Transform (SIFT), Oriented FAST and Rotated BRIEF (ORB) and histogram of oriented gradients on support vector machine as a classifier, predicts an average accuracy of 96.22% over a custom medicinal leaf dataset of 40 different species containing 2515 samples. Generate Bag of Visual Words (BoVW) by applying K-Means clustering on both SIFT and ORB descriptors to reduce the dimensionality. The combined feature vector is further analysed using random forest and k-nearest neighbor classifier. The efficacy of the proposed approach is benchmarked using Flavia dataset and artificial neural network (ANN) as a classifier. Our findings prove that the combination of local descriptors is an efficient measurement approach that benefits automatic recognition of plants based on leaf images. Also, a reliable source of medicinal leaf datasets with good quality leaf images is necessary to establish a machine learning model for medicinal plants.

Classification of Wild Animals based on SVM and Local Descriptors

In this paper, a novel method for object recognition based on hybrid local descriptors is presented. This method utilizes a combination of a few approaches (SIFT - Scale-invariant feature transform, SURF - Speeded Up Robust Features) and consists of second parts. The applicability of the presented hybrid methods are demonstrated on a few images from dataset. Dataset classes represent big animals situated in Slovak country, namely wolf, fox, brown bear, deer and wild boar. The presented method may be also used in other areas of image classification and feature extraction. The experimental results show, that the combination of local descriptors has a positive effect for object recognition.

Competitive content-based video copy detection using global descriptors

Content-Based Video Copy Detection (CBVCD) consists of detecting whether or not a video document is a copy of some known original and to retrieve the original video. CBVCD systems rely on two different tasks: Feature Extraction task, that calculates many representative descriptors for a video sequence, and Similarity Search task, that is the algorithm for finding videos in an indexed collection that match a query video. This work details a CBVCD approach based on a combination of global descriptors, an automatic weighting algorithm, a pivot-based index structure, an approximate similarity search, and a voting algorithm for copy localization. This approach is analyzed using MUSCLE-VCD-2007 corpus, and it was tested at the TRECVID 2010 evaluation together with other state-of-the-art CBVCD systems. The results show that this approach enables global descriptors to achieve competitive results and even outperforms systems based on combination of local descriptors and audio information. This approach has a potential of achieving even higher effectiveness due to its seamless ability of combining descriptors from different sources at the similarity search level.

Combination of Local Descriptors and Global Features for Leaf Recognition

Combination of Local Descriptors and Global Features for Leaf Recognition

Combination of Local Descriptors and Global Features for Leaf Recognition

Combination of Local Descriptors and Global Features for Leaf Recognition

O7.4 Molecular epidemiology of Staphylococcus aureus nasal carriage and wound colonization in a Dutch burn centre

In this paper, a novel method for object recognition based on hybrid local descriptors is presented. This method utilizes a combination of a few approaches (SIFT - Scale-invariant feature transform, SURF - Speeded Up Robust Features) and consists of second parts. The applicability of the presented hybrid methods are demonstrated on a few images from dataset. Dataset classes represent big animals situated in Slovak country, namely wolf, fox, brown bear, deer and wild boar. The presented method may be also used in other areas of image classification and feature extraction. The experimental results show, that the combination of local descriptors has a positive effect for object recognition.

Identifying Join Candidates in the Cairo Genizah

A join is a set of manuscript-fragments that are known to originate from the same original work. The Cairo Genizah is a collection containing approximately 350,000 fragments of mainly Jewish texts discovered in the late 19th century. The fragments are today spread out in libraries and private collections worldwide, and there is an ongoing effort to document and catalogue all extant fragments. The task of finding joins is currently conducted manually by experts, and presumably only a small fraction of the existing joins have been discovered. In this work, we study the problem of automatically finding candidate joins, so as to streamline the task. The proposed method is based on a combination of local descriptors and learning techniques. To evaluate the performance of various join-finding methods, without relying on the availability of human experts, we construct a benchmark dataset that is modeled on the Labeled Faces in the Wild benchmark for face recognition. Using this benchmark, we evaluate several alternative image representations and learning techniques. Finally, a set of newly-discovered join-candidates have been identified using our method and validated by a human expert.