Content-based image retrieval in DCT compressed domain with MPEG-7 edge descriptor and genetic algorithm

Phadikar, Baisakhi Sur; Phadikar, Amit; Maity, Goutam Kumar

doi:10.1007/s10044-016-0589-0

Cited by 18 publications

(10 citation statements)

References 32 publications

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“…(3) European distance improvement The local sensitive hash algorithm is used to map the high-dimensional feature data of clothing images into binary hash codes, which solves the problem of dimension disaster to some extent [16][17]. However, when the similarity measure is used to obtain the result of top-k, the comparison of hamming distance sometimes results in an unsatisfactory situation.…”

Section: Comparison Of Two Hash Methodsmentioning

confidence: 99%

Image Recommendation Algorithm Based on Deep Learning

Yin

Zhang

2020

IEEE Access

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With the rapid development of network technology, the number of digital images is growing at an alarming rate, people's demand for information gradually from text into images. However, it is very difficult for users to quickly find the images they are interested in from the large number of image libraries. The purpose of this paper is to study the image recommendation algorithm based on deep learning. In this paper, image classification algorithm is firstly studied. LReLU-Softplus activation function is formed by combining LReLU function and Softplus function, and CNN is improved. Then, an image retrieval model based on local sensitive hash algorithm is proposed in this paper. This model calculates the distance in hamming space for the binary hash code generated by mapping. Euclidean distance is calculated inside the result set after similarity measurement to improve the accuracy, and the image retrieval model is constructed. Then, an image recommendation model based on implicit support vector machine (SVM) is proposed in this paper. This image recommendation method combines image text information and image content information. The experimental results show that the proposed image recommendation model can meet the practical needs. In this paper, the overlap rate between the lsvm-based recommendation model and the human recommendation algorithm was tested, and the coincidence degree of the two recommended images reached 88%.

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Section: Comparison Of Two Hash Methodsmentioning

confidence: 99%

Image Recommendation Algorithm Based on Deep Learning

Yin

Zhang

2020

IEEE Access

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“…In this section, six studies that achieved the highest accuracy in global feature extraction will be discussed (Srivastava & Khare, 2017) (Phadikar et al, 2018), local feature extraction (Sarwar et al, 2019) (Yousuf et al, 2018), and machine learning extraction-based approaches (Tzelepi & Tefas, 2018) (Sezavar et al, 2019). All the investigated studies in this section utilize COREL dataset except studies from machines learning approach which utilizes Paris6k and ALOI.…”

Section: Comparison Among the State-of The Art Approachesmentioning

confidence: 99%

“…The second highest accuracy was achieved by (Phadikar et al, 2018). The authors proposed a CBIR system in compressed domain (Discrete Cosine Domain).…”

Section: Comparison Among the State-of The Art Approachesmentioning

confidence: 99%

Content-based image retrieval: A review of recent trends

2021

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With the availability of internet technology and the low-cost of digital image sensor, enormous amount of image databases have been created in different kind of applications. These image databases increase the demand to develop efficient image retrieval search methods that meet user requirements. Great attention and efforts have been devoted to improve content-based image retrieval method with a particular focus on reducing the semantic gap between low-level features and human visual perceptions. Due to the increasing research in this field, this paper surveys, analyses and compares the current state-of-the-art methodologies over the last six years in the CBIR field. This paper also provides an overview of CBIR framework, recent low-level feature extraction methods, machine learning algorithms, similarity measures, and a performance evaluation to inspire further research efforts.

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“…Tang et al [35] presented a neighborhood-discriminate-hashing (NDH) technique to realize the similarity search. Recently, a Genetic Algorithm (GA) based CBIR technique in the compressed domain is described in [30]. The weights of various features (i.e.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive assessment of content-based image retrieval using selected full reference image quality assessment algorithms

Phadikar

Thakur

2021

Multimed Tools Appl

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In the area of full-reference (FR) objective 'image quality assessment' (IQA), a huge amount of improvement has been done in the last few years that can calculate image quality, consistently. Contrarily, query-based image/video databases and search engines retrieve related data using 'ranking and indexing' depending on stored content. It is also to be noted that both techniques use feature extraction to achieve their goal. The efficiency of seven selected FR-IQA schemes is described in this article for the retrieval of an image signal. Extensive tests are done on two freely accessible databases. The comparison results express that mean-structural-similarity-index-measure (MSSIM), and feature-similarity-index-measure (FSIM) offer superior outcome than other IQA models. Our assessment outcome and the related thought will be very much useful for the researchers to understand the latest application areas of the IQA model in the area of image searching and retrieval.

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Content-based image retrieval in DCT compressed domain with MPEG-7 edge descriptor and genetic algorithm

Cited by 18 publications

References 32 publications

Image Recommendation Algorithm Based on Deep Learning

Image Recommendation Algorithm Based on Deep Learning

Content-based image retrieval: A review of recent trends

A comprehensive assessment of content-based image retrieval using selected full reference image quality assessment algorithms

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