Fusion framework for effective color image retrieval

Walia, Ekta; Pal, Aman

doi:10.1016/j.jvcir.2014.05.005

Cited by 96 publications

(64 citation statements)

References 30 publications

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“…Table V The results and comparisons presented in Fig. 9 and Table V indicate that the proposed late fusion of FREAK and SIFT provides a better retrieval performance than existing research [30], [42].…”

Section: Performance Evaluation Using Oliva and Torralba (Ot-scene)mentioning

confidence: 81%

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

Ali¹,

Mazhar²,

Iqbal³

et al. 2017

Preprint

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One of the challenges in Content-Based Image Retrieval (CBIR) is to reduce the semantic gaps between low-level features and high-level semantic concepts. In CBIR, the images are represented in the feature space and the performance of CBIR depends on the type of selected feature representation.Late fusion also known as visual words integration is applied to enhance the performance of image retrieval. The recent advances in image retrieval diverted the focus of research towards the use of binary descriptors as they are reported computationally efficient. In this paper, we aim to investigate the late fusion of Fast Retina Keypoint (FREAK) and Scale Invariant Feature Transform (SIFT). The late fusion of binary and local descriptor is selected because among binary descriptors, FREAK has shown good results in classification-based problems while SIFT is robust to translation, scaling, rotation and small distortions. The late fusion of FREAK and SIFT integrates the performance of both feature descriptors for an effective image retrieval. Experimental results and comparisons show that the proposed late fusion enhances the performances of image retrieval.

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Section: Performance Evaluation Using Oliva and Torralba (Ot-scene)mentioning

confidence: 81%

“…8 represents a sample of randomly selected images from each class of the OT-Scene image dataset. We evaluated the proposed framework using OT-Scene benchmark and compared the results with state-of-the-art CBIR methods [30], [42]. Fig.…”

Section: Performance Evaluation Using Oliva and Torralba (Ot-scene)mentioning

confidence: 99%

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

Ali¹,

Mazhar²,

Iqbal³

et al. 2017

Preprint

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show abstract

“…Thus, bagging [75] has been used to re-sample the data and prevent/reduce overfitting. Despite these efforts, including fusion learning as the main component of image retrieval system represents a relatively new research area for pattern recognition and image processing researchers [86,87,88]. It is expected that it will boost research for various applications based on modalities and medias such as video, audio and text.…”

Section: B Multimodal Fusion and Retrievalmentioning

confidence: 99%

A Survey on Content-based Image Retrieval

Ismail¹

2017

ijacsa

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Abstract-The widespread of smart devices along with the exponential growth of virtual societies yield big digital image databases. These databases can be counter-productive if they are not coupled with efficient Content-Based Image Retrieval (CBIR) tools. The last decade has witnessed the introduction of promising CBIR systems and promoted applications in various fields. In this article, a survey on state of the art content based image retrieval including empirical and theoretical work is proposed. This work also includes publications that cover research aspects relevant to CBIR area. Namely, unsupervised and supervised learning and fusion techniques along with lowlevel image visual descriptors have been reported. Moreover, challenges and applications that emerged to support CBIR research have been discussed in this work.

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“…The proposed approach become tested on public datasets, specifically, Wang dataset and Oliva and Torralba (OT-Scene) dataset./ Twith 3688 pix at the entire. It has outclassed the modern-day strategies in performance measures and has proven statistical significance [9].…”

Section: Rik Das Sudeep Thepade Subhajitmentioning

confidence: 99%

Design and Implementation of Retrieval of Images Based on Color, Text and Shape

Jeyalaksshmi¹,

Prasanna²

2017

International Journal of Innovative Research in Applied Sciences and Engineering

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ABSTRACT:Retrieving the image based on the content is one of the most significant research fields in the digital image processing area. The image retrieval is becoming an innovative and quickest way of retrieving the image. Many digital images and videos are uploaded and transitioned everyday, because of the social networking medium development. Google, Yahoo and many search engines are accessing the large collection of database based on the textual annotation. These images are annotated manually with keywords, descriptions and then the images are retrieved using text-based searching methods. Therefore, performances of these systems are not satisfactory. So, we need new techniques. These techniques will easy to handle the data and can easily access the data. The CBIR method is working on the basis of particular image feature such as color, texture and shape which are derived automatically. It uses digital processing and analysis to automatically generate descriptions directly from the media data. This paper represents how the implementation of image retrieval is done based on the content.

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Fusion framework for effective color image retrieval

Cited by 96 publications

References 30 publications

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

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

A Survey on Content-based Image Retrieval

Design and Implementation of Retrieval of Images Based on Color, Text and Shape

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