Unsupervised Visual Hashing with Semantic Assistant for Content-Based Image Retrieval

Zhu, Lei; Shen, Jialie; Xie, Liang; Cheng, Zhiyong

doi:10.1109/tkde.2016.2562624

Cited by 175 publications

(45 citation statements)

References 47 publications

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“…en, text enhancing the visual graph is extracted with the assistance of topic hypergraph, and the semantics information is extracted from the text information and then the hash code of image is learned which preserves the correlation of image between the semantics and images, and then at the last, the hash function code is generated within the linear aggressive model. ese desirable properties match the requirement of real application scenarios of CBIR [84].…”

Section: Cbir Research Using Deep-learning Techniquesmentioning

confidence: 85%

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

Latif

Rasheed

Sajid

et al. 2019

Mathematical Problems in Engineering

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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Section: Cbir Research Using Deep-learning Techniquesmentioning

confidence: 85%

“…Zhu et al [84] proposed unsupervised visual hashing approach known as the semantics assisted visual hashing (SAVH). is system uses two components that are offline learning and online learning.…”

Section: Cbir Research Using Deep-learning Techniquesmentioning

confidence: 99%

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

Latif

Rasheed

Sajid

et al. 2019

Mathematical Problems in Engineering

262

View full text Add to dashboard Cite

show abstract

“…Combing these two retrieval techniques is essential to improve the value of image retrieval systems. A study [12] explored the use of hashing-based image retrieval. This approach uses CBIR and computer-aided diagnosis for disease detection, diagnosis and decision support.…”

Section: Related Workmentioning

confidence: 99%

Content-based Image Retrieval of Color, Shape and Texture by Using Novel multi-SVM Classifier

Ghrabat¹,

Ma²,

Avila³

et al. 2019

IJMLC

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Background: the considerable time consumption, query retrieval difficulty and reduced retrieval rate. Still remaining challenges in Content-based image retrieval.Methods: in this work, we propose a pre-processing method that uses a Gaussian filter to improve quality by reducing image noise. An effective feature extraction method for in presented to extracted texture help color co-occurrence feature (CCF), color and shape features such as area and diameters. The colors features are extracted by means of a grey-level co-occurrence matrix and bit pattern. Extracting these features will enhance the image retrieval accuracy. With the use of a novel multi-SVM classifier, classification is performed and image retrieval is completed effectively.Results: performance measures, namely, precision, recall, error rate, correct rate, and retrieval rate, are computed. The proposed methodology produces superior results on these measures and exhibits an effective retrieval rate of approximately 94.92%; therefore, our technique is more efficient than existing MRED and MALP methods.Index Terms-Pre-processing, feature extraction, novel Multi-SVM classifier, color co-occurrence feature (CCF), grey level co-occurrence feature (GLCM), bit pattern feature (BPF).

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“…The main disadvantage is that searchable encryption used here is not sufficient. Xia, Z., and Ren, K., (2016) [38] proposed a watermarking plan that backings Content Based Image Retrieval (CBIR) over encoded pictures without releasing the data which is sensitive to cloud. This secures the protection of information in CBIR deployment applications beyond an inquisitive server and the unscrupulous inquiry clients.…”

Section: Literature Surveymentioning

confidence: 99%