Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors

Ali, Nouman; Mazhar, Danish Ali; Iqbal, Zeshan; Ashraf, Rehan; Ahmad, Jawad; Khan, Farrukh Zeeshan

doi:10.20944/preprints201703.0156.v1

Cited by 8 publications

(12 citation statements)

References 35 publications

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“…The proposed approach based on relative spatial feature extraction achieves 7.9% higher retrieval precision compared to the image retrieval based on visual words integration of SIFT and SURF [8]. Our proposed approach provides {10.25%, 2.05%} better precision and recall results compared to the late fusion based approach [26]. The experimental results demonstrate that our proposed approach significantly improves the retrieval performance compared to the state-of-the-art image retrieval techniques.…”

Section: Performance On Corel-15k Image Datasetmentioning

confidence: 82%

“…Their approach acquires the strength of both features, i.e., invariance to scale and rotation of SIFT and robustness to illumination of SURF. In another recent work, Ali et al [26] propose a late fusion of binary and local descriptors i.e., FREAK and SIFT to enhance the performance of image retrieval. Filliat et al [27] present an incremental and interactive localization and map-learning system based on BoW.…”

Section: Related Workmentioning

confidence: 99%

“…The optimal performance is obtained for a vocabulary size of 400. Table 3 provides a comparison of the mean average precision for the top 20 retrievals with the state-of-the-art image retrieval approaches [8,24,26].…”

Section: Performance On Corel-15k Image Datasetmentioning

confidence: 99%

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A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR

et al. 2018

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The requirement for effective image search, which motivates the use of Content-Based Image Retrieval (CBIR) and the search of similar multimedia contents on the basis of user query, remains an open research problem for computer vision applications. The application domains for Bag of Visual Words (BoVW) based image representations are object recognition, image classification and content-based image analysis. Interest point detectors are quantized in the feature space and the final histogram or image signature do not retain any detail about co-occurrences of features in the 2D image space. This spatial information is crucial, as it adversely affects the performance of an image classification-based model. The most notable contribution in this context is Spatial Pyramid Matching (SPM), which captures the absolute spatial distribution of visual words. However, SPM is sensitive to image transformations such as rotation, flipping and translation. When images are not well-aligned, SPM may lose its discriminative power. This paper introduces a novel approach to encoding the relative spatial information for histogram-based representation of the BoVW model. This is established by computing the global geometric relationship between pairs of identical visual words with respect to the centroid of an image. The proposed research is evaluated by using five different datasets. Comprehensive experiments demonstrate the robustness of the proposed image representation as compared to the state-of-the-art methods in terms of precision and recall values.

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Section: Performance On Corel-15k Image Datasetmentioning

confidence: 82%

Section: Related Workmentioning

confidence: 99%

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A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR

et al. 2018

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“…In last two decades, the research of CBIR is focused on different techniques that are based on computation of image spatial layout, features integration/fusion for hybrid image representation [8,9,11,16]. Among low-level image features, color is used in image analysis and retrieval to represent objects and it contains the information that can differentiate between the foreground and background.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features

Nazir¹,

Nazir²

2018

Preprint

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Due to an increase in the number of image achieves, Content-Based Image Retrieval (CBIR) has gained attention for research community of computer vision. The image visual contents are represented in a feature space in the form of numerical values that is considered as a feature vector of image. Images belonging to different classes may contain the common visuals and shapes that can result in the closeness of computed feature space of two different images belonging to separate classes. Due to this reason, feature extraction and image representation is selected with appropriate features as it directly affects the performance of image retrieval system. The commonly used visual features are image spatial layout, color, texture and shape. Image feature space is combined to achieve the discriminating ability that is not possible to achieve when the features are used separately. Due to this reason, in this paper, we aim to explore the low-level feature combination that are based on color and shape features. We selected color moments and color histogram to represent color while shape is represented by using invariant moments. We selected this combination, as these features are reported intuitive, compact and robust for image representation. We evaluated the performance of our proposed research by using the Corel, Coil and Ground Truth (GT) image datasets. We evaluated the proposed low-level feature fusion by calculating the precision, recall and time required for feature extraction. The precision, recall and feature extraction values obtained from the proposed low-level feature fusion outperforms the existing research of CBIR.

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“…According to the literature, the selection of visual features for any system is dependent on the requirements of the end user. e discriminative feature representation is another main requirement for any image retrieval system [17,18]. To make the feature more robust and unique in terms of representation fusion of low-level visual features, high computational cost is required to obtain more reliable results [19,20].…”

Section: Introductionmentioning

confidence: 99%

Content‐Based Image Retrieval and Feature Extraction: A Comprehensive Review

Latif

Rasheed

Sajid

et al. 2019

Mathematical Problems in Engineering

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262

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Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.

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Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors

Cited by 8 publications

References 35 publications

A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR

A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR

An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features

Content‐Based Image Retrieval and Feature Extraction: A Comprehensive Review

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