The Technique of Shape-Based Multi-Feature Combination of TradeMark Image Retrieval

At present, the technique of trademark image retrieval based on multi-feature combination of the shape mainly includes single-feature global matching or local matching and multi-feature matching, which is playing a more and more important role in the area of the trademark image retrieval. In this paper, due to the deficiency described by some single shape-based features, the technique of the multi-feature combination trademark image retrieval is proposed based on the region and the edge of a shape. Firstly, a trademark image is segmented with region growing, then low order Hu moments and eccentricity are extracted on the resulting region, which is able to express the local information of the image; Secondly, there is an extraction of Compactness and Convexity, which describe the global feature of the image, on the edge extracted with Canny. At last, the combination of the multi-feature is applied to get a Euclidean distance. Good results have been obtained in the following experiment, which proves the multi-feature combination way is better than other single-feature ways.

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The Technique of Spatial Relationships-Based and Shape-Based Multi-Feature Combination of Trademark Image Retrieval

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.171-172.36 ◽

2010 ◽

Vol 171-172 ◽

pp. 36-40 ◽

Cited By ~ 1

Author(s):

Cong Zhang ◽

Fu Cheng You

Keyword(s):

Image Retrieval ◽

Similarity Measure ◽

Euler Number ◽

Trade Mark ◽

Spatial Relationships ◽

Feature Combination ◽

Single Feature ◽

Trademark Images ◽

The Relationship ◽

Better Than

There is no doubt that the technique of trademark image retrieval based on multi-feature is increasingly popular. It’s proposed that a combination of the feature of spatial relationships and shape-based is used to retrieve trade mark images in this paper. Firstly, angle relations describe the relationship each spatial object and a special similarity measure is used; Secondly, in order to well describe the shape features of the trademark images, a Euler-number of shape-region-based and compactness and convexity based on shape-contour are proposed; Thirdly, the similarity measure of an inquiring image and database images in the total number of objects is applied with the combination of multi-feature. Good results have been obtained in the following experiments, which prove the multi-feature combination way is better than other single-feature ways.

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An Image Retrieval Method Based on Local Features of Interest Points

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.3789 ◽

2011 ◽

Vol 121-126 ◽

pp. 3789-3793

Author(s):

Xiang Fu ◽

Jun Ting Wang ◽

Jie Xian Zeng

Keyword(s):

Image Retrieval ◽

Feature Matching ◽

Local Features ◽

Local Information ◽

Interest Points ◽

Background Region ◽

Retrieval Method ◽

Matching Techniques ◽

Traditional Image

To measure the similarity between two images, the local information around all the points were usually used for traditional image retrieval methods based on interest points. The accuracy would be influenced by the dissimilar interest points in the background region or in the uninterested regions. A new image retrieval method based on interest points was proposed in this paper. The most similar interest points were chosen firstly based on feature matching techniques, then the similarity was measured based on the local gray information around the preserved similar interest points. Experiments show that the proposed method is more accurate than traditional image retrieval method based on interest points.

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Non-rigid feature matching for image retrieval using global and local regularizations

2017 IEEE International Conference on Multimedia and Expo (ICME) ◽

10.1109/icme.2017.8019441 ◽

2017 ◽

Author(s):

Yong Ma ◽

Huabing Zhou ◽

Jun Chen ◽

Jingshu Shi ◽

Zhongyuan Wang

Keyword(s):

Image Retrieval ◽

Feature Matching ◽

Global And Local

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Trajectory Tracking For Quadrotors: An Optimization-Based Planning Followed By Controlling Approach

10.21203/rs.3.rs-963714/v2 ◽

2021 ◽

Author(s):

Geesara Kulathunga ◽

Dmitry Devitt ◽

Alexandr Klimchik

Keyword(s):

Trajectory Tracking ◽

Euclidean Distance ◽

Control Policy ◽

Multiple Shooting ◽

Reference Trajectory ◽

Linear Quadratic ◽

Linear Quadratic Gaussian ◽

Distance Transformation ◽

Direct Collocation ◽

Better Than

Abstract We present an optimization-based reference trajectory tracking method for quadrotor robots for slow-speed maneuvers. The proposed method uses planning followed by the controlling paradigm. The basic concept of the proposed method is an analogy to Linear Quadratic Gaussian (LQG) in which Nonlinear Model Predictive Control (NMPC) is employed for predicting optimal control policy in each iteration. Multiple-shooting (MS) is suggested over Direct-collocation (DC) for imposing constraints when modelling the NMPC. Incremental Euclidean Distance Transformation Map (EDTM) is constructed for obtaining the closest free distances relative to the predicted trajectory; these distances are considered obstacle constraints. The reference trajectory is generated, ensuring dynamic feasibility. The objective is to minimize the error between the quadrotor’s current pose and the desired reference trajectory pose in each iteration. Finally, we evaluated the proposed method with two other approaches and showed that our proposal is better than those two in terms of reaching the goal without any collision. Additionally, we published a new dataset, which can be used for evaluating the performance of trajectory tracking algorithms.

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Studi Akurasi Karakteristik Retina sebagai Future Identification dengan Euclidean Distance Metrics

Informatika Mulawarman Jurnal Ilmiah Ilmu Komputer ◽

10.30872/jim.v16i1.5136 ◽

2021 ◽

Vol 16 (1) ◽

pp. 19

Author(s):

Suhendro Yusuf Irianto ◽

Ribut Yulianto ◽

Sri Karnila ◽

Dona Yuliawati

Keyword(s):

Image Retrieval ◽

Euclidean Distance ◽

Distance Metrics

Penelitian ini menghasilkan sistem keamanan menggunakan biometrik, dengan menggunakan retina sebagai identitas pengenalan yang akurat, serta efektif untuk meningkatkan proses identifikasi pada retina dimasa depan (future identification). Hal ini sangat penting untuk menentukan keakuratan sifat biometrik apa yang paling baik di dalam proses mengidentifikasi di masa depan, sekaligus membangun suatu sistem aplikasi atau tools yang dapat digunakan untuk mengetahui karakteristik distance meterics untuk mengukur akurasi retina sebagai identitas dimasa depan (future identification). Penggunaan retina dapat menjadi salah satu alternatif identifikasi manusia seperti untuk pengganti PIN ATM Bank, Paspor dan bidang-bidang lain yang memerlukan tingkat keamanan tinggi atau mustahil untuk dapat dipalsukan. Hasil dari penelitian ini ialah berbentuk pengujian untuk membuktikan tingkat akurasi CBIR dengan menggunakan citra query dengan dibangun database sebanyak 5.000 citra retina. Metode yang akan digunakan dalam menentukan similarity dan identification dengan menggunakan fitur warna. Histogram warna untuk pencarian citra dikerjakan dengan mengitung jumlah koefisien DCT dari setiap warna. Hasil penelitian menunjukan bahwa akurasi algoritma mendekati nilai 90%, akurasi ini cukup bagus di bidang image retrieval. Di lihat dari kecepatan proses retrieval juga cukup cepat dimana rata –rata kecepatan proses dengan menggunakan 2.000 citra digital adalah kurang dari 10 detik.

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DEEP LEARNING BASED FEATURE MATCHING AND ITS APPLICATION IN IMAGE ORIENTATION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-25-2020 ◽

2020 ◽

Vol V-2-2020 ◽

pp. 25-33 ◽

Cited By ~ 1

Author(s):

L. Chen ◽

F. Rottensteiner ◽

C. Heipke

Keyword(s):

Deep Learning ◽

Feature Matching ◽

Shape Estimation ◽

Feature Description ◽

Performance Benchmark ◽

Matching Performance ◽

Affine Shape ◽

Image Orientation ◽

Learned Features ◽

Better Than

Abstract. Matching images containing large viewpoint and viewing direction changes, resulting in large perspective differences, still is a very challenging problem. Affine shape estimation, orientation assignment and feature description algorithms based on detected hand crafted features have shown to be error prone. In this paper, affine shape estimation, orientation assignment and description of local features is achieved through deep learning. Those three modules are trained based on loss functions optimizing the matching performance of input patch pairs. The trained descriptors are first evaluated on the Brown dataset (Brown et al., 2011), a standard descriptor performance benchmark. The whole pipeline is then tested on images of small blocks acquired with an aerial penta camera, to compute image orientation. The results show that learned features perform significantly better than alternatives based on hand crafted features.

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Fully Automated Pose Estimation of Historical Images in the Context of 4D Geographic Information Systems Utilizing Machine Learning Methods

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10110748 ◽

2021 ◽

Vol 10 (11) ◽

pp. 748

Author(s):

Ferdinand Maiwald ◽

Christoph Lehmann ◽

Taras Lazariv

Keyword(s):

Information Systems ◽

Geographic Information Systems ◽

Image Retrieval ◽

Pose Estimation ◽

Large Scale ◽

Feature Matching ◽

Geographic Information ◽

Content Based Image Retrieval ◽

Matching Methods ◽

Historical Images

The idea of virtual time machines in digital environments like hand-held virtual reality or four-dimensional (4D) geographic information systems requires an accurate positioning and orientation of urban historical images. The browsing of large repositories to retrieve historical images and their subsequent precise pose estimation is still a manual and time-consuming process in the field of Cultural Heritage. This contribution presents an end-to-end pipeline from finding relevant images with utilization of content-based image retrieval to photogrammetric pose estimation of large historical terrestrial image datasets. Image retrieval as well as pose estimation are challenging tasks and are subjects of current research. Thereby, research has a strong focus on contemporary images but the methods are not considered for a use on historical image material. The first part of the pipeline comprises the precise selection of many relevant historical images based on a few example images (so called query images) by using content-based image retrieval. Therefore, two different retrieval approaches based on convolutional neural networks (CNN) are tested, evaluated, and compared with conventional metadata search in repositories. Results show that image retrieval approaches outperform the metadata search and are a valuable strategy for finding images of interest. The second part of the pipeline uses techniques of photogrammetry to derive the camera position and orientation of the historical images identified by the image retrieval. Multiple feature matching methods are used on four different datasets, the scene is reconstructed in the Structure-from-Motion software COLMAP, and all experiments are evaluated on a newly generated historical benchmark dataset. A large number of oriented images, as well as low error measures for most of the datasets, show that the workflow can be successfully applied. Finally, the combination of a CNN-based image retrieval and the feature matching methods SuperGlue and DISK show very promising results to realize a fully automated workflow. Such an automated workflow of selection and pose estimation of historical terrestrial images enables the creation of large-scale 4D models.

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Combining Block DCV and Support Vector Machine for Ear Recognition

International Journal of Interdisciplinary Telecommunications and Networking ◽

10.4018/ijitn.2016040104 ◽

2016 ◽

Vol 8 (2) ◽

pp. 36-44 ◽

Cited By ~ 1

Author(s):

Zhao Hailong ◽

Yi Junyan

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Image Retrieval ◽

Construction Method ◽

Experimental Results ◽

Support Vector ◽

Recognition Method ◽

Ear Recognition ◽

Human Ear ◽

Better Than

In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.

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Image Retrieval Using the Intensity Variation Descriptor

Mathematical Problems in Engineering ◽

10.1155/2020/6283987 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 4

Author(s):

Zhao Wei ◽

Guang-Hai Liu

Keyword(s):

Image Retrieval ◽

Information Extraction ◽

Lateral Inhibition ◽

Intensity Variation ◽

Spatial Layout ◽

Inhibition Mechanism ◽

Square Root ◽

Optimal Direction ◽

Selective Mechanism ◽

Better Than

Variations between image pixel characteristics contain a wealth of information. Extraction of such cues can be used to describe image content. In this paper, we propose a novel descriptor, called the intensity variation descriptor (IVD), to represent variations in colour, edges, and intensity and apply it to image retrieval. The highlights of the proposed method are as follows. (1) The IVD combines the advantages of the HSV and RGB colour spaces. (2) It can simulate the lateral inhibition mechanism and orientation-selective mechanism to determine an optimal direction and spatial layout. (3) An extended weighted L1 distance metric is proposed to calculate the similarity of images. It does not require complex operations such as square or square root and leads to good performance. Comparative experiments on two Corel datasets containing 15,000 images show that the proposed method performs better than the SoC-GMM, CPV-THF, and STH methods and provides good matching of texture, colour, and shape.

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