Content based image retrieval for multi-objects fruits recognition using k-means and k-nearest neighbor

Erwin, Erwin; Fachrurrozi, Muhammad; Fiqih, Ahmad; Saputra, Bahardiansyah Rua; Algani, Rachmad; Primanita, Anggina

doi:10.1109/icodse.2017.8285855

Cited by 15 publications

(9 citation statements)

References 10 publications

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“…One of the most used clustering methods is k-means. The idea of the k-means [14] algorithm is to minimize the distance between objects in the clusters. The algorithm stops working when the further minimization becomes.…”

Section: Processing Of the Image Preparation Resultsmentioning

confidence: 99%

Methods of Semantic Integrity Preservation in the Pattern Recognition Process

Kim

Matveeva

Viksnin

et al. 2019

International Journal of Embedded and Real-Time Communication Systems

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Computer vision is a wide area of theoretical research and technical methods connected with object detection, object tracking and object classification. In this article computer vision is considered in context of embedding it into automobiles in order to automate the road traffic process through video stream analysis. During road traffic it is vital to detect objects quickly and correctly, so the authors pay attention to the pattern recognition quality, especially to the visual information semantic integrity preservation. Their main purpose is to find the ways of its possible improvement respectively to three basic stages of the pattern recognition process. To avoid semantic integrity violations of information in the initial stage of the image analysis the authors propose normalization; in the second stage new clustering method was developed based on particle swarm optimization and k-means algorithm; in the final stage of the pattern recognition process the Haar cascade classifier was used with normalized training samples. The obtained image processing algorithm was implemented in case of blurred and noisy images and proved its effectiveness respectively to the visual information semantic integrity preservation.

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Section: Processing Of the Image Preparation Resultsmentioning

confidence: 99%

Methods of Semantic Integrity Preservation in the Pattern Recognition Process

Kim

Matveeva

Viksnin

et al. 2019

International Journal of Embedded and Real-Time Communication Systems

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“…Klasifikasi gambar digunakan untuk membuat aplikasi pengenalan objek. Misalnya adalah Teks dan karakter pengakuan pada lembaran logam [1], Pembelajaran mendalam untuk pengenalan karakter Jawa tulisan tangan [2], Pengambilan gambar berbasis konten untuk pengenalan buah multi-objek menggunakan k-means dan k-terdekat tetangga [3], Lisensi Pengenalan Plat Otomatis berdasarkan deteksi tepi [4] dan Arab pengenalan tulisan tangan menggunakan optimasi minimal sekuensial [5].…”

Section: Pendahuluanunclassified

Klasifikasi Topeng Pandawa dengan SVM

Sanjaya

Setyati

Budianto

2020

Journal Of Information Technology

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Klasifikasi merupakan tahapan tingkat lanjut dari sebuah keilmuan computer vision. Karena tujuan dari sebuah aplikasi rekognisi yaitu mengenali. Cara mengenali yaitu dengan cara klasifikasi. Banyak metode klasifikasi yang ada, namun pada penelitian ini menggunakan Support Vector Machine (SVM). SVM dipilih karena bisa mengatasi data dengan dimensi yang sangat besar tanpa mereduksi data, bekerja dengan data linier atau nonlinier dan membuat sebuah hyperplane yang memisahkan data antar kelas. Pada penelitian ini menggunakan data patung pandawa dengan lima kelas. Lima kelas terdiri dari kelas yudhistira, bima, arjuna, nakula dan sadewa. Kernel yang digunakan pada penelitian ini menggunakan Radial Basis Function (RBF). Hasil ujicoba pada penelitian mempunya rata-rata akurasi sebesar 0,848.

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“…Contrast can also be referred to as the variance of the sum of squares (sum of squares variance). The formula for calculating the Contrast (CON), can be seen in the following equation (3).…”

Section: B Feature Extractionmentioning

confidence: 99%

“…Classification of images is used to create an object recognition applications. For example are Text and character recognition on metal-sheets [1], Deep learning for handwritten Javanese character recognition [2], Content-based image retrieval for multi-objects fruits recognition using k-means and k-nearest neighbor [3], License Automatic Plate Recognition based on edge detection [4] and Arabic handwriting recognition using sequential minimal optimization [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Digit Classification of Majapahit Relic Inscription using GLCM-SVM

2018

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A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %.

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Content based image retrieval for multi-objects fruits recognition using k-means and k-nearest neighbor

Cited by 15 publications

References 10 publications

Methods of Semantic Integrity Preservation in the Pattern Recognition Process

Methods of Semantic Integrity Preservation in the Pattern Recognition Process

Klasifikasi Topeng Pandawa dengan SVM

Digit Classification of Majapahit Relic Inscription using GLCM-SVM

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