Impact of reduction in descriptor size on object detection and classification

Rani, Ritu; Singh, Amit Prakash; Kumar, Ravinder

doi:10.1007/s11042-018-6911-7

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…Generally, to find the object-of-interest on an image, OD utilizes distinctive shape patterns as evidence [15]. For describing an object on an image, a crucial role is played by the selection as well as extraction of distinct key points in object recognition applications [16]. OD is considered to be a crucial and an active field in Information and Technology which prompted the curiosity among many researchers.…”

Section: Introductionmentioning

confidence: 99%

Efficient Object Detection and Classification Approach Using HTYOLOV4 and M2RFO-CNN

Arulalan¹,

Kumar²

2023

Computer Systems Science and Engineering

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Object detection and classification are the trending research topics in the field of computer vision because of their applications like visual surveillance. However, the vision-based objects detection and classification methods still suffer from detecting smaller objects and dense objects in the complex dynamic environment with high accuracy and precision. The present paper proposes a novel enhanced method to detect and classify objects using Hyperbolic Tangent based You Only Look Once V4 with a Modified Manta-Ray Foraging Optimizationbased Convolution Neural Network. Initially, in the pre-processing, the video data was converted into image sequences and Polynomial Adaptive Edge was applied to preserve the Algorithm method for image resizing and noise removal. The noiseless resized image sequences contrast was enhanced using Contrast Limited Adaptive Edge Preserving Algorithm. And, with the contrast-enhanced image sequences, the Hyperbolic Tangent based You Only Look Once V4 was trained for object detection. Additionally, to detect smaller objects with high accuracy, Grasp configuration was observed for every detected object. Finally, the Modified Manta-Ray Foraging Optimization-based Convolution Neural Network method was carried out for the detection and the classification of objects. Comparative experiments were conducted on various benchmark datasets and methods that showed improved accurate detection and classification results.

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Section: Introductionmentioning

confidence: 99%

Efficient Object Detection and Classification Approach Using HTYOLOV4 and M2RFO-CNN

Arulalan¹,

Kumar²

2023

Computer Systems Science and Engineering

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“…Feature reduction is operated by two main methods: feature selection, which filters the irrelevant or redundant features from an original feature dataset and maintains a subset of the original feature dataset, and feature extraction, which creates a new feature dataset. Feature extraction identifies the dominant features or attributes of the dataset, through for example, principle components analysis [14], linear discriminant analysis (LDA) [15], and autoencoders [16]- [18]. By reducing the number of features that describe the dataset, feature extraction also increases the speed of machine learning techniques such as classification techniques.…”

Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Jaruenpunyasak

Duangsoithong

2021

IEEE Access

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Deep learning algorithms are employed in many applications, especially in medical fields such as gait analysis and human pose detection for rehabilitation. However, creating the desired model with deep learning algorithms requires high memory and computing costs, which is problematic because deep learning technologies must be run on low-power devices such as edge computing equipment. To deal with these problems, feature reduction methods reduce the memory and energy costs. This paper presents an empirical analysis of deep learning with feature reduction. The method classifies foot images for knee rehabilitation using convolutional and dense autoencoders. The obtained results are compared with those of conventional methods (histograms of oriented gradients and local binary pattern algorithms). The features were classified and compared using support vector machine, k-nearest neighbor, and multilayer perceptron methods. The experimental results demonstrate that the conventional method uses fewer features than the deep learning method with higher accuracy because its algorithm projects pixels onto the histogram. In addition, using fewer features in deep learning layers maintains high accuracy, which is beneficial for edge computing implementations.

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“…These studies can be grouped into two main approaches: The first approach aims to reduce the complexity of the matching algorithm without losing precision. A principal component analysis (PCA) [19] and linear discriminant analysis [36] are dimensionality reduction techniques that reduce the size of the original descriptor, such as SIFT or SURF [26], [37]. Calonder et al [38] proposed a concept that used a shorten descriptor to quantize its floating-point coordinates into integers codes on fewer bits; the same result was proposed in [39] and [40].…”

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confidence: 99%

Ground Control Point Automatic Extraction for Spaceborne Georeferencing Based on FPGA

Liu

Zhou

Zhang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Feature points that are obtained from the combined speeded-up robust feature (SURF) detector and binary robust independent elementary features (BRIEF) descriptor have a highly robust performance. These points are previously considered the ground control points (GCPs) for building a connection between the image coordinates and the corresponding geodetic coordinates. This article proposes a novel architecture to automatically and intelligently extract GCPs based on field programmable gate arrays (FPGAs). The parallelization SURF detector, BRIEF descriptor, and BRIEF matching are implemented in a single Xilinx XC7VX980T FPGA system. Word length reduction, memoryefficient parallel architecture, shift and subtraction strategies, a sliding window for separable convolution, and an optimized multispacer-scale are used to optimize the SURF detector. Improved parallel adder trees are used to accelerate the BRIEF matching. The proposed system achieves 380 frame per second (fps) with a 100 MHz clock frequency, which satisfies the real-time and low-power requirements of embedded devices. The results of the experiment demonstrate that the proposed architecture, when mapped onto a Xilinx Virtex-7 XC7VX980T FPGA device, can select the robust feature points. Index Terms-Binary robust independent elementary features (BRIEF), field programmable gate arrays (FPGAs), georeferencing, ground control points (GCPs), speeded-up robust feature (SURF). I. INTRODUCTION G EOREFERENCING is an important technique of remote sensing (RS) image for an extensive variety of tasks, and estimation of the mathematical geometric calibration function of RS image based on the ground control points (GCPs) is necessary. Traditionally, GCPs are obtained by special equipment Manuscript

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Impact of reduction in descriptor size on object detection and classification

Cited by 6 publications

References 22 publications

Efficient Object Detection and Classification Approach Using HTYOLOV4 and M2RFO-CNN

Efficient Object Detection and Classification Approach Using HTYOLOV4 and M2RFO-CNN

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Ground Control Point Automatic Extraction for Spaceborne Georeferencing Based on FPGA

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