A new fusion approach for content based image retrieval with color histogram and local directional pattern

Zhou, Juxiang; Liu, Xiaodong; Xu, Tao; Gan, Jianhou; Liu, Wanquan

doi:10.1007/s13042-016-0597-9

Cited by 49 publications

(32 citation statements)

References 42 publications

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“…In order to validate the performance of the proposed method, some state-of-the-art methods were selected for comparison: SoC-GMM [55], LDP [56], SSH [1], CPV-THF [57], and STH [58]. A distance metric and benchmark dataset were required for the comparisons.…”

Section: Resultsmentioning

confidence: 99%

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Image Retrieval Using the Intensity Variation Descriptor

Zhao

Liu

2020

Mathematical Problems in Engineering

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Variations between image pixel characteristics contain a wealth of information. Extraction of such cues can be used to describe image content. In this paper, we propose a novel descriptor, called the intensity variation descriptor (IVD), to represent variations in colour, edges, and intensity and apply it to image retrieval. The highlights of the proposed method are as follows. (1) The IVD combines the advantages of the HSV and RGB colour spaces. (2) It can simulate the lateral inhibition mechanism and orientation-selective mechanism to determine an optimal direction and spatial layout. (3) An extended weighted L1 distance metric is proposed to calculate the similarity of images. It does not require complex operations such as square or square root and leads to good performance. Comparative experiments on two Corel datasets containing 15,000 images show that the proposed method performs better than the SoC-GMM, CPV-THF, and STH methods and provides good matching of texture, colour, and shape.

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Section: Resultsmentioning

confidence: 99%

“…Here, we compare the proposed method with state-of-the-art methods such as SSH [1], SoC-GMM [55], LDP [56], CPV-THF [57], and STH [58].…”

Section: Performance Comparisonsmentioning

confidence: 99%

Image Retrieval Using the Intensity Variation Descriptor

Zhao

Liu

2020

Mathematical Problems in Engineering

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“…Tthe performance of distance measures used for comparison are, Euclidean distance, cityblock distance, cosine distance and hamming distances. These distance measures are used with four different conventional features, maLBP, mdLBP, LBP [7] and RGB [8]. maLBP stands for multi channel adder Local binary pattern, mdLBP-multi channel decoder local binary pattern, LBP-local binary patter.…”

Section: Proposed Methodsmentioning

confidence: 99%

Performance Analysis of Distance Metric for Content Based Image Retrieval

M O*,

E R

2019

IJEAT

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Content based image retrieval uses different feature descriptors for image search and retrieval. For image retrieval from huge image repositories, the query image features are extracted and compares these features with the contents of feature repository. The most matching image is found and retrieved from the database. This mapping is done based on the distance calculated between feature vector of query image and the extracted feature vectors of images in the database. There are various distance measures used for comparing image feature vectors. This paper compares a set of distance measures using a set of features used for CBIR. The city-block distance measure gives the best results for CBIR.This paper implemented different distance measures over the wang's dataset and compared the results obtained for content based image retrieval using four features. The features used are, maLBP, mdLBP, LBP and RGB. The results infer that the cityblock distance measure gives the best results for CBIR. City block distance measure got an average improvement of 12.575%, 21.202, 36.035% and 15.083% improvement when the maLBP, mdLBP, LBP and RGB features respectively are used for retrieval.Experience: 17 years of teaching and research experience.

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“…The color histogram is one of the most direct and the most effective color feature representation [19]. But it lacks spatial information.…”

Section: Calculation Of Color Histogrammentioning

confidence: 99%

Minority Costume Image Retrieval by Fusion of Color Histogram and Edge Orientation Histogram

Shen¹,

Zhou²,

Xu³

2016

IJNDC

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It has very important practical significance to analyze and research minority costume from the perspective of computer vision for minority culture protection and inheritance. As first exploration in minority costume image retrieval, this paper proposed a novel image feature representation method to describe the rich information of minority costume image. Firstly, the color histogram and edge orientation histogram are calculated for divided subblocks of minority costume image. Then, the final feature vector for minority costume image is formed by effective fusion of color histogram and edge orientation histogram. We have evaluated the performances of the proposed algorithm on self-build minority costume image dataset, and the experimental results show that our method can effectively express the integrated feature of minority costume images, including color, texture, shape and spatial information. Compared with some conventional methods, our method has higher and stable retrieval accuracy.

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A new fusion approach for content based image retrieval with color histogram and local directional pattern

Cited by 49 publications

References 42 publications

Image Retrieval Using the Intensity Variation Descriptor

Image Retrieval Using the Intensity Variation Descriptor

Performance Analysis of Distance Metric for Content Based Image Retrieval

Minority Costume Image Retrieval by Fusion of Color Histogram and Edge Orientation Histogram

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