scholarly journals Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4449
Author(s):  
Federico Magliani ◽  
Laura Sani ◽  
Stefano Cagnoni ◽  
Andrea Prati
Keyword(s):  
Genetic Algorithm ◽  
Image Retrieval ◽  
Image Features ◽  
Retrieval Performance ◽  
Retrieval Task ◽  
K Nearest Neighbors ◽  
Diffusion Parameters ◽  
Recent Computer ◽  
Mobile Vision ◽  
And Performance

Most recent computer vision tasks take into account the distribution of image features to obtain more powerful models and better performance. One of the most commonly used techniques to this purpose is the diffusion algorithm, which fuses manifold data and k-Nearest Neighbors (kNN) graphs. In this paper, we describe how we optimized diffusion in an image retrieval task aimed at mobile vision applications, in order to obtain a good trade-off between computation load and performance. From a computational efficiency viewpoint, the high complexity of the exhaustive creation of a full kNN graph for a large database renders such a process unfeasible on mobile devices. From a retrieval performance viewpoint, the diffusion parameters are strongly task-dependent and affect significantly the algorithm performance. In the method we describe herein, we tackle the first issue by using approximate algorithms in building the kNN tree. The main contribution of this work is the optimization of diffusion parameters using a genetic algorithm (GA), which allows us to guarantee high retrieval performance in spite of such a simplification. The results we have obtained confirm that the global search for the optimal diffusion parameters performed by a genetic algorithm is equivalent to a massive analysis of the diffusion parameter space for which an exhaustive search would be totally unfeasible. We show that even a grid search could often be less efficient (and effective) than the GA, i.e., that the genetic algorithm most often produces better diffusion settings when equal computing resources are available to the two approaches. Our method has been tested on several publicly-available datasets: Oxford5k, ROxford5k, Paris6k, RParis6k, and Oxford105k, and compared to other mainstream approaches.

scholarly journals Implementation of early and late fusion methods for content-based image retrieval

2021 ◽  
Vol 8 (7) ◽  
pp. 97-105
Author(s):  
Ali Ahmed ◽  
◽  
Sara Mohamed ◽  
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Image Features ◽  
Distance Measures ◽  
Content Based Image Retrieval ◽  
Late Fusion ◽  
Query Image ◽  
Fusion Methods ◽  
And Performance ◽  
Image Repository

Content-Based Image Retrieval (CBIR) systems retrieve images from the image repository or database in which they are visually similar to the query image. CBIR plays an important role in various fields such as medical diagnosis, crime prevention, web-based searching, and architecture. CBIR consists mainly of two stages: The first is the extraction of features and the second is the matching of similarities. There are several ways to improve the efficiency and performance of CBIR, such as segmentation, relevance feedback, expansion of queries, and fusion-based methods. The literature has suggested several methods for combining and fusing various image descriptors. In general, fusion strategies are typically divided into two groups, namely early and late fusion strategies. Early fusion is the combination of image features from more than one descriptor into a single vector before the similarity computation, while late fusion refers either to the combination of outputs produced by various retrieval systems or to the combination of different rankings of similarity. In this study, a group of color and texture features is proposed to be used for both methods of fusion strategies. Firstly, an early combination of eighteen color features and twelve texture features are combined into a single vector representation and secondly, the late fusion of three of the most common distance measures are used in the late fusion stage. Our experimental results on two common image datasets show that our proposed method has good performance retrieval results compared to the traditional way of using single features descriptor and also has an acceptable retrieval performance compared to some of the state-of-the-art methods. The overall accuracy of our proposed method is 60.6% and 39.07% for Corel-1K and GHIM-10K ‎datasets, respectively.

Text-Image Retrieval With Salient Features

2021 ◽  
Vol 32 (4) ◽  
pp. 1-13
Author(s):  
Xia Feng ◽  
Zhiyi Hu ◽  
Caihua Liu ◽  
W. H. Ip ◽  
Huiying Chen
Keyword(s):  
Image Retrieval ◽  
Recall Rate ◽  
Image Features ◽  
Feature Representation ◽  
Image Feature ◽  
Text And Image ◽  
Retrieval Task ◽  
Retrieval Method ◽  
Salient Features ◽  
Object Level

In recent years, deep learning has achieved remarkable results in the text-image retrieval task. However, only global image features are considered, and the vital local information is ignored. This results in a failure to match the text well. Considering that object-level image features can help the matching between text and image, this article proposes a text-image retrieval method that fuses salient image feature representation. Fusion of salient features at the object level can improve the understanding of image semantics and thus improve the performance of text-image retrieval. The experimental results show that the method proposed in the paper is comparable to the latest methods, and the recall rate of some retrieval results is better than the current work.

scholarly journals A Grid Feature-Point Selection Method for Large-Scale Street View Image Retrieval Based on Deep Local Features

2020 ◽  
Vol 12 (23) ◽  
pp. 3978
Author(s):  
Tianyou Chu ◽  
Yumin Chen ◽  
Liheng Huang ◽  
Zhiqiang Xu ◽  
Huangyuan Tan
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Local Features ◽  
Selection Method ◽  
Image Features ◽  
Feature Point ◽  
Feature Points ◽  
Point Selection ◽  
Retrieval Task ◽  
Street View

Street view image retrieval aims to estimate the image locations by querying the nearest neighbor images with the same scene from a large-scale reference dataset. Query images usually have no location information and are represented by features to search for similar results. The deep local features (DELF) method shows great performance in the landmark retrieval task, but the method extracts many features so that the feature file is too large to load into memory when training the features index. The memory size is limited, and removing the part of features simply causes a great retrieval precision loss. Therefore, this paper proposes a grid feature-point selection method (GFS) to reduce the number of feature points in each image and minimize the precision loss. Convolutional Neural Networks (CNNs) are constructed to extract dense features, and an attention module is embedded into the network to score features. GFS divides the image into a grid and selects features with local region high scores. Product quantization and an inverted index are used to index the image features to improve retrieval efficiency. The retrieval performance of the method is tested on a large-scale Hong Kong street view dataset, and the results show that the GFS reduces feature points by 32.27–77.09% compared with the raw feature. In addition, GFS has a 5.27–23.59% higher precision than other methods.

scholarly journals J-EDA: A workbench for tuning similarity and diversity search parameters in content-based image retrieval

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
João V. O. Novaes ◽  
Lúcio F. D. Santos ◽  
Luiz Olmes Carvalho ◽  
Daniel De Oliveira ◽  
Marcos V. N. Bedo ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Metric Spaces ◽  
Human Interaction ◽  
Fine Tuning ◽  
Distance Functions ◽  
Content Based Image Retrieval ◽  
Retrieval Task ◽  
K Nearest Neighbors ◽  
Retrieval Problem ◽  
Feedback Techniques

Similarity searches can be modeled by means of distances following the Metric Spaces Theory and constitute a fast and explainable query mechanism behind content-based image retrieval (CBIR) tasks. However, classical distance-based queries, e.g., Range and k-Nearest Neighbors, may be unsuitable for exploring large datasets because the retrieved elements are often similar among themselves. Although similarity searching is enriched with the imposition of rules to foster result diversification, the fine-tuning of the diversity query is still an open issue, which is is usually carried out with and a non-optimal expensive computational inspection. This paper introduces J-EDA, a practical workbench implemented in Java that supports the tuning of similarity and diversity search parameters by enabling the automatic and parallel exploration of multiple search settings regarding a user-posed content-based image retrieval task. J-EDA implements a wide variety of classical and diversity-driven search queries, as well as many CBIR settings such as feature extractors for images, distance functions, and relevance feedback techniques. Accordingly, users can define multiple query settings and inspect their performances for spotting the most suitable parameterization for a content-based image retrieval problem at hand. The workbench reports the experimental performances with several internal and external evaluation metrics such as P × R and Mean Average Precision (mAP), which are calculated towards either incremental or batch procedures performed with or without human interaction.

Figure Based Biomedical Document Retrieval System using Structural Image Features

2012 ◽  
Vol 3 (1) ◽  
pp. 39-58
Author(s):  
Harikrishna G. N. Rai ◽  
K Sai Deepak ◽  
P. Radha Krishna
Keyword(s):  
Structural Properties ◽  
Retrieval System ◽  
Document Retrieval ◽  
Image Features ◽  
Biomedical Literature ◽  
Feature Descriptor ◽  
Retrieval Performance ◽  
Retrieval Task ◽  
Edge Information ◽  
Structural Image

Multi-modal and Unstructured nature of documents make their retrieval from healthcare document repositories a challenging task. Text based retrieval is the conventional approach used for solving this problem. In this paper, the authors explore an alternate avenue of using embedded figures for the retrieval task. Usually, context of a document is directly reflected in the associated figures, therefore embedded text within these figures along with image features have been used for similarity based retrieval of figures. The present work demonstrates that image features describing the structural properties of figures are sufficient for the figure retrieval task. First, the authors analyze the problem of figure retrieval from biomedical literature and identify significant classes of figures. Second, they use edge information as a means to discriminate between structural properties of each figure category. Finally, the authors present a methodology using a novel feature descriptor namely Fourier Edge Orientation Autocorrelogram (FEOAC) to describe structural properties of figures and build an effective Biomedical document retrieval system. The experimental results demonstrate the better retrieval performance and overall improvement of FEOAC for figure retrieval task, especially when most of the edge information is retained. Apart from invariance to scale, rotation and non-uniform illumination, the proposed feature descriptor is shown to be relatively robust to noisy edges.

Automatic Feature Weight Assignment Based on Image Retrieval Using Genetic Algorithm

2014 ◽  
Vol 931-932 ◽  
pp. 1402-1406 ◽  
Author(s):  
Purichaya Srisook ◽  
Kata Praditwong
Keyword(s):  
Genetic Algorithm ◽  
Image Retrieval ◽  
Texture Features ◽  
Image Features ◽  
New Method ◽  
Content Based Image Retrieval ◽  
Equal Weight ◽  
Optimal Weight ◽  
Optimal Value ◽  
Feature Weight

The work proposes the new method to increase an efficiency of a Content-based Image Retrieval (CBIR) system. For combining many image features, the optimal weight of each feature is required. To find the optimal value of the feature, this work uses Genetic Algorithm (GA). An image is represented as color, shape and texture features. The experiment compares the results from the system with equal weight values and the system with the weights provided by GA. Evaluation shows the robustness and efficiency of the proposed technique.

scholarly journals Aggregated Deep Local Features for Remote Sensing Image Retrieval

2019 ◽  
Vol 11 (5) ◽  
pp. 493 ◽  
Author(s):  
Raffaele Imbriaco ◽  
Clint Sebastian ◽  
Egor Bondarev ◽  
Peter de With
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Query Expansion ◽  
State Of The Art ◽  
Expansion Method ◽  
Remote Sensing Image ◽  
Retrieval Performance ◽  
Retrieval Task ◽  
Global Representation ◽  
Current Systems

Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requires no external inputs. Experiments demonstrate that even without training, the local convolutional features and global representation outperform other systems. After system tuning, we can achieve state-of-the-art or competitive results. Furthermore, we observe that our query expansion method increases overall system performance by about 3%, using only the top-three retrieved images. Finally, we show how dimensionality reduction produces compact descriptors with increased retrieval performance and fast retrieval computation times, e.g., 50% faster than the current systems.

scholarly journals A ten-year literature review of content-based image retrieval (CBIR) studies in the tourism industry

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chanattra Ammatmanee ◽  
Lu Gan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Relevant Literature ◽  
Image Features ◽  
Tourism Industry ◽  
Fine Tuning ◽  
Content Based Image Retrieval ◽  
Comparative Approach ◽  
Retrieval Task ◽  
Content Type

Purpose Due to the worldwide growth of digital image sharing and the maturity of the tourism industry, the vast and growing collections of digital images have become a challenge for those who use and/or manage these image data across tourism settings. To overcome the image indexing task with less labour cost and improve the image retrieval task with less human errors, the content-based image retrieval (CBIR) technique has been investigated for the tourism domain particularly. This paper aims to review the relevant literature in the field to understand these previous works and identify research gaps for future directions. Design/methodology/approach A systematic and comprehensive review of CBIR studies in tourism from the year 2010 to 2019, focussing on journal articles and conference proceedings in reputable online databases, is conducted by taking a comparative approach to critically analyse and address the trends of each fundamental element in these research experiments. Findings Based on the review of the literature, the trends of CBIR studies in tourism is to improve image representation and retrieval by advancing existing feature extraction techniques, contributing novel techniques in the feature extraction process through fine-tuning fusion features and improving image query of CBIR systems. Co-authorship, tourist attraction sector and fusion image features have been in focus. Nonetheless, the number of studies in other tourism sectors and available image databases could be further explored. Originality/value The fact that no existing academic review of CBIR studies in tourism makes this paper a novel contribution.

Research on Industry Image Technology with the Integration Algorithm of Color Features and Feedback of Image Retrieval

2014 ◽  
Vol 1014 ◽  
pp. 520-524 ◽  
Author(s):  
Shui Li Zhang
Keyword(s):  
Image Retrieval ◽  
Feedback Mechanism ◽  
Image Features ◽  
Feature Integration ◽  
Retrieval Performance ◽  
Integration Algorithm ◽  
Retrieval Method ◽  
Current Image ◽  
Color Features ◽  
The Individual

Current image retrieval methods use only one kind of image features, which can not describe image content completely. In this paper, an image retrieval method based on color and texture integration is proposed. Calculation of similarity for the individual features is normalized before color feature integration. In order to improve the precision of image retrieval, a relevance feedback mechanism is invoked. The experiment results show that the proposed method has an excellent retrieval performance.

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