Indonesian fruits classification from image using MPEG‐7 descriptors and ensemble of simple classifiers

Fruits classification from image is a very challenging task, particularly for Indonesian indigenous fruits, due to some similarities occurred in several types of the fruits. This study proposes a method to classify Indonesian fruits from image using MPEG‐7 color and texture descriptors. The descriptors were directly extracted from the image without pre‐processing and segmentation steps. Principle component analysis was then applied to reduce the dimension of the descriptors. Four simple classifiers, decision tree, naïve Bayesian, linear discriminant analysis, and k‐nearest neighbor were used to classify the fruit image based on extracted descriptors. An ensemble of simple classifiers trained with some combination of MPEG‐7 descriptors has been constructed to increase the classification accuracy of single simple classifier. To validate the proposed method, an Indonesian fruit images data set consisted of 15 classes was developed in this study. The experiment result showed that the ensemble of simple classifiers achieved the best accuracy of 97.80% by employing linear discriminant analysis, and k‐nearest neighbor as base classifiers trained using CSD, SCD, and the combination of CLD and EHD. Therefore, the proposed method achieved a good classification accuracy and can be applied in vision‐based classification system in industry

Effective classifiers for detecting objects

Several state-of-the-art machine learning classifiers are compared for the purposes of object detection in complex images, using global image features derived from the Ohta color space and Local Binary Patterns. Image complexity in this sense refers to the degree to which the target objects are occluded and/or non-dominant (i.e. not in the foreground) in the image, and also the degree to which the images are cluttered with non-target objects. The results indicate that a voting ensemble of Support Vector Machines, Random Forests, and Boosted Decision Trees provide the best performance with AUC values of up to 0.92 and Equal Error Rate accuracies of up to 85.7% in stratified 10-fold cross validation experiments on the GRAZ02 complex image dataset

Automating the construction of scene classifiers for content-based video retrieval

This paper introduces a real time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a two stage procedure. First, small image fragments called patches are classified. Second, frequency vectors of these patch classifications are fed into a second classifier for global scene classification (e.g., city, portraits, or countryside). The first stage classifiers can be seen as a set of highly specialized, learned feature detectors, as an alternative to letting an image processing expert determine features a priori. We present results for experiments on a variety of patch and image classes. The scene classifier has been used successfully within television archives and for Internet porn filtering

Evaluation of Statistical Features for Medical Image Retrieval

In this paper we present a complete system allowing the classification of medical images in order to detect possible diseases present in them. The proposed method is developed in two distinct stages: calculation of descriptors and their classification. In the first stage we compute a vector of thirty-three statistical features: seven are related to statistics of the first level order, fifteen to that of second level where thirteen are calculated by means of co-occurrence matrices and two with absolute gradient; the last thirteen finally are calculated using run-length matrices. In the second phase, using the descriptors already calculated, there is the actual image classification. Naive Bayes, RBF, Support VectorMa- chine, K-Nearest Neighbor, Random Forest and Random Tree classifiers are used. The results obtained from the proposed system show that the analysis carried out both on textured and on medical images lead to have a high accuracy