Local Image Description Research Articles

Revisiting Binary Local Image Description for Resource Limited Devices

The advent of a panoply of resource limited devices opens up new challenges in the design of computer vision algorithms with a clear compromise between accuracy and computational requirements. In this letter we present new binary image descriptors that emerge from the application of triplet ranking loss, hard negative mining and anchor swapping to traditional features based on pixel differences and image gradients. These descriptors, BAD (Box Average Difference) and HashSIFT, establish new operating points in the state-of-the-art's accuracy vs. resources trade-off curve. In our experiments we evaluate the accuracy, execution time and energy consumption of the proposed descriptors. We show that BAD bears the fastest descriptor implementation in the literature while HashSIFT approaches in accuracy that of the top deep learning-based descriptors, being computationally more efficient. We have made the source code public <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

Analysis of Tuberculosis in Chest Radiographs for Computerized Diagnosis using Bag of Keypoint Features.

Chest radiography is the most preferred non-invasive imaging technique for early diagnosis of Tuberculosis (TB). However, lack of radiological expertise in TB detection leads to indiscriminate chest radiograph (CXR) screening. A modest classification approach based on the local image description to detect subtle characteristics of TB using CXRs is highly recommended. In this work, an attempt has been made to classify normal and TB CXR images using Bag of Features (BoF) approach with Speeded-Up Robust Feature (SURF) descriptor. The images are obtained from a public database. Lung fields segmentation is performed using Distance Regularized Level Set (DRLS) formulation. The results of segmentation are validated against the ground truth images using similarity, overlap and area correlation measures. BoF approach with SURF keypoint descriptors is implemented to categorize the images using Multilayer Perceptron (MLP) classifier. The obtained results demonstrate that the DRLS method is able to delineate lung fields from CXR images. The BoF with SURF keypoint descriptor is able to characterize local attributes of normal and TB images. The segmentation results are found to be in high correlation with ground truth. MLP classifier is found to provide high Recall, Specificity (Spec), Accuracy, F-score and Area Under the Curve (AUC) values of 87.7%, 85.9%, 87.8%, 87.6% and 94% respectively between normal and abnormal images. The proposed computer aided diagnostic approach is found to perform better as compared to the existing methods. Thus, the study can be of significant assistance to physicians at the point of care in resource constrained regions.

Center Symmetric Local Descriptors for Image Classification

Local feature description forms an integral part of texture classification, image recognition, and face recognition. In this paper, the authors propose Center Symmetric Local Ternary Mapped Patterns (CS-LTMP) and eXtended Center Symmetric Local Ternary Mapped Patterns (XCS-LTMP) for local description of images. They combine the strengths of Center Symmetric Local Ternary Pattern (CS-LTP) which uses ternary codes and Center Symmetric Local Mapped Pattern (CS-LMP) which captures the nuances between images to make the CS-LTMP. Similarly, the auhtors combined CS-LTP and eXtended Center Symmetric Local Mapped Pattern (XCS-LMP) to form eXtended Center Symmetric Local Ternary Mapped Pattern (XCS-LTMP). They have conducted their experiments on the CIFAR10 dataset and show that their proposed methods perform significantly better than their direct competitors.

Model-Based Referenceless Quality Metric of 3D Synthesized Images Using Local Image Description.

New challenges have been brought out along with the emerging of 3D-related technologies, such as virtual reality, augmented reality (AR), and mixed reality. Free viewpoint video (FVV), due to its applications in remote surveillance, remote education, and so on, based on the flexible selection of direction and viewpoint, has been perceived as the development direction of next-generation video technologies and has drawn a wide range of researchers' attention. Since FVV images are synthesized via a depth image-based rendering (DIBR) procedure in the "blind" environment (without reference images), a reliable real-time blind quality evaluation and monitoring system is urgently required. But existing assessment metrics do not render human judgments faithfully mainly because geometric distortions are generated by DIBR. To this end, this paper proposes a novel referenceless quality metric of DIBR-synthesized images using the autoregression (AR)-based local image description. It was found that, after the AR prediction, the reconstructed error between a DIBR-synthesized image and its AR-predicted image can accurately capture the geometry distortion. The visual saliency is then leveraged to modify the proposed blind quality metric to a sizable margin. Experiments validate the superiority of our no-reference quality method as compared with prevailing full-, reduced-, and no-reference models.

Embedded platform for local image descriptor based object detection

The article presents novel idea of a hardware accelerated image processing algorithm for embedded systems. The system is based on the well known Fast Retina Keypoint (FREAK) local image description algorithm. The solution utilizes Field Programmable Gate Array (FPGA) as a flexible module that is used to implement hardware acceleration of a given part of the image processing algorithm. The approach presented in this paper is slightly different. Since we are using very fast FREAK descriptor it is not our purpose to implement full feature extraction algorithm in hardware but just its most time-consuming part which is brute force matcher based on the Hamming distance. Moreover our goal was to design very flexible system so that the feature detection and extraction algorithm can be replaced without any interruption in the hardware accelerated part.

Compact Local Directional Texture Pattern for Local Image Description

This paper presents an effective local image feature region descriptor, called CLDTP descriptor (Compact Local Directional Texture Pattern), and its application in image matching and object recognition. The CLDTP descriptor encodes the directional and contrast information in a local region, so it contains the gradient orientation information and the gradient magnitude information. As the dimension of the CLDTP histogram is much lower than the dimension of the LDTP histogram, the CLDTP descriptor has higher computational efficiency and it is suitable for image matching. Extensive experiments have validated the effectiveness of the designed CLDTP descriptor.

Local image region description using orthogonal symmetric local ternary pattern

Local image description is a key issue for local features related tasks in computer vision. A good descriptor should achieve two competing goals which are high-quality description and low computational complexity. In this paper, we propose a novel operator called orthogonal symmetric local ternary pattern (OS-LTP), achieving robustness against noise interference and discriminative ability for describing texture structure. Then, based on the proposed OS-LTP operator, we introduce a new descriptor, named weighted orthogonal symmetric local ternary pattern (WOS-LTP). Unlike traditional descriptors, the WOS-LTP descriptor is constructed by using the OS-LTP variance of the local region as an adaptive weight to adjust the contribution of the OS-LTP code in histogram calculation. Extensive experimental results demonstrate the effectiveness and efficiency of the new descriptor compared with existing state-of-the-art descriptors.