Á trous gradient structure descriptor for content based image retrieval

Agarwal, Megha; Maheshwari, R. P.

doi:10.1007/s13735-012-0005-5

Cited by 16 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Avg. recall Prop.method 0.785 0.838 [9] 0.630 N/A [27] 0.727 N/A [42] 0.783 0.156 [43] 0.697 0.130 [1] 0.602 0.449 [35] 0.640 0.416 [33] 0.683 0.540 [38] 0.707 0.490 [31] 0.750 0.560…”

Section: Methodsmentioning

confidence: 99%

Detecting Visual Objects by Edge Crawling

Grycuk

Wojciechowski

Wei

et al. 2020

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

AbstractContent-based image retrieval methods develop rapidly with a growing scale of image repositories. They are usually based on comparing and indexing some image features. We developed a new algorithm for finding objects in images by traversing their edges. Moreover, we describe the objects by histograms of local features and angles. We use such a description to retrieve similar images fast. We performed extensive experiments on three established image datasets proving the effectiveness of the proposed method.

show abstract

“…Avg. recall Prop.method 0.785 0.838 [9] 0.630 N/A [27] 0.727 N/A [42] 0.783 0.156 [43] 0.697 0.130 [1] 0.602 0.449 [35] 0.640 0.416 [33] 0.683 0.540 [38] 0.707 0.490 [31] 0.750 0.560…”

Section: Methodsmentioning

confidence: 99%

Detecting Visual Objects by Edge Crawling

Grycuk

Wojciechowski

Wei

et al. 2020

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

show abstract

“…Wavelet correlogram is one such technique suggested by Tarzan et al [19] that utilizes the concept of a genetic algorithm. A combination of a trous wavelet and MSD (Microstructure Descriptor) to form the feature vector was put forward by Agarwal et al [20] called a Trous gradient structure descriptor for image retrieval.…”

Section: Related Workmentioning

confidence: 99%

Content-Based Image Retrieval System Based on Fusion of Wavelet Transform, Texture and Shape Features

Kishore¹,

Rao²

2021

MMEP

View full text Add to dashboard Cite

In the last few years, Content-Based Image Retrieval (CBIR) has received wide attention. Compared to text-based image retrieval contents of the image are more in information for efficient retrieval by Content-Based Image Retrieval. The single feature cannot be applied to all the images and provides lower performance. In this paper, we have put forward a proposal on an image retrieval using multi-feature fusion. The concept of multi-resolution has been exploited with the help of a wavelet transform. This method combines Local Binary Pattern (LBP) with Fast and Accurate Exponent Fourier Moments (FAEFM’s) with the wavelet decomposition of an image using multiple resolutions. In order to extract the feature of texture from image, LBP codes of Discrete Wavelet Transform (DWT), the image coefficients are estimated followed by the computation of Fast and Accurate Exponent Fourier Moments to these LBP codes so as to extract features of shape to construct the required feature vector. These constructed vectors aid us in exactly finding out and retrieving visually similar images from existing databases. The benchmark databases Corel-1k and Olivia 2688 are used to test the proposed method. The proposed method achieves 99.99% of precision and 93.15% of recall on Corel-1k database and 99.99% of precision and recall of 93.63% on Olivia-2688 database, which are higher than the existing methods.

show abstract

“…It is because of this property that wavelets have been used extensively for image retrieval. The combination of á trous wavelet with microstructure descriptor (MSD) as á trous gradient structure descriptor has been proposed in [22]. Wang et al [8] incorporated colour, texture and shape features for image retrieval.…”

Section: Related Workmentioning

confidence: 99%

Content-Based Image Retrieval Using Moments of Local Ternary Pattern

2014

View full text Add to dashboard Cite

Due to the availability of large number of digital images, development of an efficient content-based indexing and retrieval method is required. Also, the emergence of smartphones and modern PDAs has further substantiated the need of such systems. This paper proposes a combination of Local Ternary Pattern (LTP) and moments for Content-Based Image Retrieval. Image is divided into blocks of equal size and LTP codes of each block are computed. Geometric moments of LTP codes of each block are computed followed by computation of distance between moments of LTP codes of query and database images. Then, the threshold using distance values is applied to retrieve images similar to the query image. Performance of the proposed method is compared with other state-of-the-art methods on the basis of results obtained on Corel-1,000 database. The comparison shows that the proposed method gives better results in terms of precision and recall as compared to other state-of-the-art image retrieval methods.

show abstract

Á trous gradient structure descriptor for content based image retrieval

Cited by 16 publications

References 22 publications

Detecting Visual Objects by Edge Crawling

Detecting Visual Objects by Edge Crawling

Content-Based Image Retrieval System Based on Fusion of Wavelet Transform, Texture and Shape Features

Content-Based Image Retrieval Using Moments of Local Ternary Pattern

Contact Info

Product

Resources

About