A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern

Giveki, Davar; Soltanshahi, Mohammad Ali; Montazer, Gholam Ali

doi:10.1016/j.ijleo.2016.11.046

Cited by 60 publications

(25 citation statements)

References 39 publications

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“…However, since reducing the semantic gap between the computed low level features of an image and high level semantics is still highly challenging issue owing to semantic labels which fails in expressing the whole visual characteristics of an image. Hence, semantic based image retrieval is so far significantly limited in accuracy [6][7][8]. However, to address this semantic gap, researchers in the domain of computer vision are working towards biologically inspired feature for better discrimination of an image [7].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, semantic based image retrieval is so far significantly limited in accuracy [6][7][8]. However, to address this semantic gap, researchers in the domain of computer vision are working towards biologically inspired feature for better discrimination of an image [7]. Thus, image retrieval based on visual contents like color, texture and shape become booming over the past two decades and more vigorous research domain for the multimedia researchers.…”

Section: Introductionmentioning

confidence: 99%

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An efficient content based image retrieval using enhanced multi-trend structure descriptor

Sathiamoorthy

Mohananthini

2020

SN Appl. Sci.

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This paper contributes a new variant of multi-trend structure descriptor (MTSD) for efficient content based image retrieval. The proposed variant of MTSD encodes color/edge orientation/texture quantized values versus orientation of equal, small and large trends instead of color/edge orientation/texture quantized values versus equal, small and large trends. In addition, it also encodes color/edge orientation/texture quantized values versus average location of distribution of pixel values for equal, small and large trends at each orientation. To reduce the time cost of the proposed variant of MTSD with the preservation of its accuracy, the image is decomposed into fine level using discrete Haar wavelet transform and the fine level for the decomposition of an image is determined empirically. Comprehensive experiments are conducted using the benchmark Corel-1k, Corel-5k, Corel-10k, Caltech-101, LIDC-IDRI-CT, VIA/I-ELCAP-CT and OASIS-MRI image datasets and the results evident that the proposed variant of MTSD achieves the state-of-the-art performance for natural, textural and biomedical image retrieval. Precision and recall are the measures used to measure the accuracy. Euclidean similarity measure is used to calculate the similarity information between query and target images.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An efficient content based image retrieval using enhanced multi-trend structure descriptor

Sathiamoorthy

Mohananthini

2020

SN Appl. Sci.

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“…As there was no prior knowledge which C and γ were acceptable, they were determined based on a recursive loop with different combinations. During the process, a good pair of C and γ could be identified [38].…”

Section: Image Data Preprocessingmentioning

confidence: 99%

Detection of Firmiana danxiaensis Canopies by a Customized Imaging System Mounted on an UAV Platform

Liu

Zhuo

et al. 2018

Journal of Sensors

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The objective of this study was to test the effectiveness of mapping the canopies of Firmiana danxiaensis (FD), a rare and endangered plant species in China, from remotely sensed images acquired by a customized imaging system mounted on an unmanned aerial vehicle (UAV). The work was conducted in an experiment site (approximately 10 km 2 ) at the foot of Danxia Mountain in Guangdong Province, China. The study was conducted as an experimental task for a to-be-launched large-scale FD surveying on Danxia Mountain (about 200 km 2 in area) by remote sensing on UAV platforms. First, field-based spectra were collected through hand-held hyperspectral spectroradiometer and then analyzed to help design a classification schema which was capable of differentiating the targeted plant species in the study site. Second, remote-sensed images for the experiment site were acquired and calibrated through a variety of preprocessing steps. Orthoimages and a digital surface model (DSM) were generated as input data from the calibrated UAV images. The spectra and geometry features were used to segment the preprocessed UAV imagery into homogeneous patches. Lastly, a hierarchical classification, combined with a support vector machine (SVM), was proposed to identify FD canopies from the segmented patches. The effectiveness of the classification was evaluated by on-site GPS recordings. The result illustrated that the proposed hierarchical classification schema with a SVM classifier on the remote sensing imagery collected by the imaging system on UAV provided a promising method for mapping of the spatial distribution of the FD canopies, which serves as a replacement for field surveys in the attempt to realize a wide-scale plant survey by the local governments.

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“…They usually include BRISK, FAST, SIFT, SURF and Harris corner points [28], [53], [61]. SIFT points have been used in this work because they are invariant to image scale, translations and rotations [62]. In addition, they are robust with perspective transformations and moderate illumination variations [63].…”

Section: ) Keypointsmentioning

confidence: 99%

Image Processing Based Ambient Context-Aware People Detection and Counting

Habeeb¹,

Vuksanović²,

Habeeb³

2018

IJMLC

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Abstract-Different technologies are employed to detect and count people in various situations but crowd counting system based on computer vision is one of the best choices due to a number of advantages. These include accuracy, flexibility, cost and acquiring people distribution information. Crowd counting system based on computer vision can use closed circuit television cameras (CCTV) that have already become ubiquitous and their uses are increasing. This paper aims to develop crowd counting system that can be incorporated with existing CCTV cameras. In this paper, the extracted low-level features in a frame-to-frame analysis are processed using regression technique to estimate the number of people. Two complex scenes and environments are used to evaluate the performances of the proposed system. The results have shown that the proposed system can achieve good performance in terms of the mean absolute error (MAE) and mean squared error (MSE).

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A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern

Cited by 60 publications

References 39 publications

An efficient content based image retrieval using enhanced multi-trend structure descriptor

An efficient content based image retrieval using enhanced multi-trend structure descriptor

Detection of Firmiana danxiaensis Canopies by a Customized Imaging System Mounted on an UAV Platform

Image Processing Based Ambient Context-Aware People Detection and Counting

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