Automatic fruit and vegetable classification from images

Abstract

Contemporary Vision and Pattern Recognition problems such as face recognition, fingerprinting identification, image categorization, and DNA sequencing often have an arbitrarily large number of classes and properties to consider. To deal with such complex problems using just one feature descriptor is a difficult task and feature fusion may become mandatory. Although normal feature fusion is quite effective for some problems, it can yield unexpected classification results when the different features are not properly normalized and preprocessed. Besides it has the drawback of increasing the dimensionality which might require more training data. To cope with these problems, this paper introduces a unified approach that can combine many features and classifiers that requires less training and is more adequate to some problems than a naïve method, where all features are simply concatenated and fed independently to each classification algorithm. Besides that, the presented technique is amenable to continuous learning, both when refining a learned model and also when adding new classes to be discriminated. The introduced fusion approach is validated using a multi-class fruit-and-vegetable categorization task in a semi-controlled environment, such as a distribution center or the supermarket cashier. The results show that the solution is able to reduce the classification error in up to 15 percentage points with respect to the baseline.

Color description of low resolution images using fast bitwise quantization and border-interior classification

“…An example of such reduction is shown in Figure 3. The original studies used images with 354 × 256 for COREL [5] and a downsampling to 640 × 480 for Produce [6].…”

“…It was acquired using RGB color images with 1024 × 786 pixels [6]. In the original work, the authors achieved good classification performance using a pipeline of operations: i) downsampling by half of the original size using linear interpolation, ii) background subtraction based on k-Means algorithm, iii) extraction of five different feature vectors and iv) feature/classifier fusion.…”

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

“…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.…”

Short communication. Computer vision applied to saffron flower (Crocus sativus L.) processing

“…Computer vision is becoming more and more used in industry for fruits (Blasco et al, 2008;Rakun et al, 2011), vegetables (Story et al, 2010 or even both (Rocha et al, 2010). These related works are focused from inspection (Story et al, 2010) to manipulation (Huang and Lee, 2010) either outdoors in the field (Swain et al, 2010), inside a greenhouse (Story et al, 2010) or in a research laboratory (Omid et al, 2010).…”

“…fruit/ vegetable classification) as much as possible (Rocha et al, 2010;Unay et al, 2011) and increasing the product quality using some kind of anticipation (Story et al, 2010). For this purpose, commercial robots (Huang and Lee, 2010) and mechanisms specially designed for the particular application (Story et al, 2010;Swain et al, 2010) are used together with image processing of color (Rocha et al, 2010;Swain et al, 2010) or monochrome images (Huang and Lee, 2010). Furthermore, highlevel programming languages like Delphi or C++ (Huang and Lee, 2010;Swain et al, 2010) and vision libraries like Matrox Image Library (Huang and Lee, 2010) are also used.…”