Sub-block Features Based Image Retrieval

Sajwan, Vijaylakshmi; Goyal, Puneet

doi:10.1007/978-81-322-2205-7_59

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…In order to get the information of color spatial distribution, many reserchers have proposed different ways to segment the image, such as in paper [6][7][8]. In this paper, we divided International Conference on Information Technology and Management Innovation (ICITMI 2015) the image into nine unequal blocks according to the proportion 1:2:1 of image length and width.…”

Section: Color Feature Extractionmentioning

confidence: 99%

Image Retrieval Based on Weighted Block Color Histogram and Texton Co-occurrece Matrix

Huang¹,

Dai²,

Wu³

2015

Proceedings of the 4th International Conference on Information Technology and Management Innovation

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Keywords: image retrieval; HSV color histogram; texton co-occurrence matrix; feature extraction. Abstract. Image retrieval is an important topic in the fields of image processing, pattern recognition and computer vision. In this paper, we proposed a new and efficient method of image retreival by combining the color and texture features. We used HSV color histogram for color feature extraction and texton co-occurrence matrix for texture feature extraction. In color feature extraction, the image was divided into nine unequal blocks first and then for each block HSV color histogram was computed. In texture feature extraction, the gray level image was filtered with four predefined textons first and then we used co-occurrence matrix to extract the texture feature of the filtered texton image. After feature extraction, we used Euclidean distance to measure the similarity of the query image and images in the database. We emploied Corel 1000 database for our experiment, the experimental results show that our proposed method performs better than the previous methods.

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Section: Color Feature Extractionmentioning

confidence: 99%

Image Retrieval Based on Weighted Block Color Histogram and Texton Co-occurrece Matrix

Huang¹,

Dai²,

Wu³

2015

Proceedings of the 4th International Conference on Information Technology and Management Innovation

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“…LBP is a significant descriptor that represents local texture characteristics that are untouched by variations in lighting. The LBP operator has been used to classify textures [25,27], recognize faces [29][30][31], recognize facial expressions [32,33], and retrieve images [34][35][36][37], among other things. The bulk of research on the classic LBP descriptor and its variants have been performed on grayscale images.…”

Section: Introductionmentioning

confidence: 99%

Color Texture Description with Diagonal Local Binary Patterns Using a New Distance Metric for Impressive Image Retrieval

Sajwan¹,

Ranjan²,

Swapnil³

2021

INDJCSE

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With the reliability and dependence of the world over the digitalization of information in all spectrums of professional and personal life, the volume of digital data is increasing exponentially. With such a huge database, retrieving information, and images from the storage media pose serious challenges. In this paper, a diagonal local binary pattern for color images (DLBPC) and a new distance metric are proposed for the retrieval of data with high accuracy and speed. The descriptor employs the deviceindependent L*a*b* color space. The discriminative quality of DLBPC is enhanced by combining local features with the (color moment) global feature of the image for effective image retrieval. To assess the efficacy, the suggested system was implemented on challenging datasets: Wang -1K and GHIM-10K. The analysis findings demonstrate the efficacy of the suggested system over other reported systems.

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Sub-block Features Based Image Retrieval

Cited by 2 publications

References 7 publications

Image Retrieval Based on Weighted Block Color Histogram and Texton Co-occurrece Matrix

Image Retrieval Based on Weighted Block Color Histogram and Texton Co-occurrece Matrix

Color Texture Description with Diagonal Local Binary Patterns Using a New Distance Metric for Impressive Image Retrieval

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