Automatic Produce Classification from Images Using Color, Texture and Appearance Cues

Rocha, Anderson; Hauagge, Daniel Cabrini; Wainer, Jacques; Goldenstein, Siome

doi:10.1109/sibgrapi.2008.9

Cited by 17 publications

(13 citation statements)

References 20 publications

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“…An approach to identify fruit and vegetable in supermarket is introduced in [8]. First, the image color is described using global measures which are histograms, mean, contrast, homogeneity, energy, variance, correlation, and entropy over the histograms for each color channel.…”

Section: Related Workmentioning

confidence: 99%

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Automatic Fruit Image Recognition System Based on Shape and Color Features

Zawbaa

Abbass

Hazman

et al. 2014

Communications in Computer and Information Science

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Abstract. This paper presents an automatic fruit recognition system for classifying and identifying fruit types. The work exploits the fruit shape and color, to identify each image feature. The proposed system includes three phases namely: pre-processing, feature extraction, and classification phases. In the pre-processing phase, fruit images are resized to 90 x 90 pixels in order to reduce their color index. In feature extraction phase, the proposed system uses scale invariant feature transform (SIFT) and shape and color features to generate a feature vector for each image in the dataset. For classification phase, the proposed model applies K-Nearest Neighborhood (K-NN) algorithm classification, and support vector machine (SVM) algorithm of different kinds of fruits. A series of experiments were carried out using the proposed model on a dataset of 178 fruit images. The results of carrying out these experiments demonstrate that the proposed approach is capable of automatically recognize the fruit name with a high degree of accuracy.

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Section: Related Workmentioning

confidence: 99%

“…While, the border/interior pixel classifier (BIC) is used to classify the image border. The appearance feature is obtained using a vocabulary of parts which is found using K-means and a bottom-up clustering algorithms [8].…”

Section: Related Workmentioning

confidence: 99%

Automatic Fruit Image Recognition System Based on Shape and Color Features

Zawbaa

Abbass

Hazman

et al. 2014

Communications in Computer and Information Science

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“…Several other researchers have proposed methods to recognize natural produce using computer vision by employing a combination of long features and complex classifiers in order to achieve high recognition performance [9][10][11]. However, more time is needed to extract the long features and to train the complex classifiers in these proposed methods.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Neural Network and Linear Model for Natural Produce Recognition Using Computer Vision

et al. 2017

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Abstract. Natural produce recognition is a classification problem with various applications in the food industry. This paper proposes a natural produce recognition method using computer vision. The proposed method uses simple features consisting of statistical color features and the derivative of radius function. A hybrid neural network and linear model based on a Kalman filter (NN-LMKF) was employed as classifier. One thousand images from ten categories of natural produce were used to validate the proposed method by using 5-fold cross validation. The experimental result showed that the proposed method achieved classification accuracy of 98.40%. This means it performed better than the original neural network and k-nearest neighborhood.

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“…This leads us to the question, how well the MSM-based methods perform in classifying 3D objects with appearances so similar that even human vision has difficulty classifying them? There are many types of 3D objects with such characteristics in various practical applications; flaw inspection of industrial component, quality checking, and screening of fruits and vegetables [9] [10].…”

Section: Introductionmentioning

confidence: 99%

Classification of Similar 3D Objects with Different Types of Features from Multi-view Images

Niigaki

Fukui

2009

Advances in Image and Video Technology

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This paper proposes a method for classifying 3D objects with similar appearances using different types of features from multi-view images. We can find this type of task in various practical applications, such as flaw inspection of industrial components, quality checking, ans screening of fruits and vegetables. In this paper, as an example such a concrete task, we will deal with the problem of classifying apples, a task that is difficult even for human vision. To tackle this task, we will introduce the mutual subspace method (MSM)-based methods as a weak classifiers in an ensemble learning framework. In addition, we will consider three types of features: shape, texture and color in the terms of invariants of position and scale, as input vectors of each MSM-based classifier. The effectiveness of the proposed method will be demonstrated through the results of evaluation experiments using 100 apples.

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Automatic Produce Classification from Images Using Color, Texture and Appearance Cues

Cited by 17 publications

References 20 publications

Automatic Fruit Image Recognition System Based on Shape and Color Features

Automatic Fruit Image Recognition System Based on Shape and Color Features

Hybrid Neural Network and Linear Model for Natural Produce Recognition Using Computer Vision

Classification of Similar 3D Objects with Different Types of Features from Multi-view Images

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