Search

This work presents a generic computer vision system designed for exploiting trained deep Convolutional Neural Networks (CNN) as a generic feature extractor and mixing these features with more traditional hand-crafted features. Such a system is a single structure that can be used for synthesizing a large number of different image classification tasks. Three substructures are proposed for creating the generic computer vision system starting from handcrafted and non-handcrafter features: i) one that remaps the output layer of a trained CNN to classify a different problem using an SVM; ii) a second for exploiting the output of the penultimate layer of a trained CNN as a feature vector to feed an SVM; and iii) a third for merging the output of some deep layers, applying a dimensionality reduction method, and using these features as the input to an SVM. The application of feature transform techniques to reduce the dimensionality of feature sets coming from the deep layers represents one of the main contributions of this paper. Three approaches are used for the non-handcrafted features: deep transfer learning features based on convolutional neural networks (CNN), principal component analysis network (PCAN), and the compact binary descriptor (CBD). For the handcrafted features, a wide variety of state-of-the-art algorithms are considered: Local Ternary Patterns, Local Phase Quantization, Rotation Invariant Co-occurrence Local Binary Patterns, Completed Local Binary Patterns, Rotated local binary pattern image, Globally Rotation Invariant Multi-scale Co-occurrence Local Binary Pattern, and several others. The computer vision system based on the proposed approach was tested on many different datasets, demonstrating the generalizability of the proposed approach thanks to the strong performance recorded. The Wilcoxon signed rank test is used to compare the different methods; moreover, the independence of the different methods is studied using the Q-statistic. To facilitate replication of our experiments, the MATLAB source code will be available at ( https://www.dropbox.com/s/bguw035yrqz0pwp/ElencoCode.docx?dl=0 ). [ABSTRACT FROM AUTHOR]

In this work, we propose an unorthodox approach for describing a given image. Each image is represented by a feature vector whose elements are the scores assigned to object classes by deep convolutional neural networks that were not related to those that built the given image classification problem. The deep neural networks are trained using 1000 classes; therefore, each image is described by 1000 scores, which are fed to a support vector machine. The proposed approach could be considered a transfer learning method, where, instead of repurposing the learned features to a second classification problem, we use the scores obtained by trained convolutional neural networks. Methods based on state of the art handcrafted descriptors, and the novel approach presented here are compared, together with selected ensembles of such methods. The fusion between a standard approach and the new unorthodox method boosts the performance of the standard approach. The Wilcoxon signed rank test is used to compare the different methods. The novel method is applied to 21 different datasets to demonstrate its generality. The MATLAB source code to replicate our experiments will be available at ( https://www.dei.unipd.it/node/2357 +Pattern Recognition and Ensemble Classifiers). [ABSTRACT FROM AUTHOR]

An ensemble of approaches for reliable person re-identification is proposed in this paper. The proposed ensemble is built combining widely used person re-identification systems using different color spaces and some variants of state-of-the-art approaches that are proposed in this paper. Different descriptors are tested, and both texture and color features are extracted from the images; then the different descriptors are compared using different distance measures (e.g., the Euclidean distance, angle, and the Jeffrey distance). To improve performance, a method based on skeleton detection, extracted from the depth map, is also applied when the depth map is available. The proposed ensemble is validated on three widely used datasets (CAVIAR4REID, IAS, and VIPeR), keeping the same parameter set of each approach constant across all tests to avoid overfitting and to demonstrate that the proposed system can be considered a general-purpose person re-identification system. Our experimental results show that the proposed system offers significant improvements over baseline approaches. The source code used for the approaches tested in this paper will be available at https://www.dei.unipd.it/node/2357 and http://robotics.dei.unipd.it/reid/ . [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effects that different preprocessing techniques have on the performance of features extracted from Haralick's co-occurrence matrix, one of the best known methods for analyzing image texture. In addition, we compare and combine different strategies for extracting descriptors from the co-occurrence matrix. We propose an ensemble of different preprocessing methods, where, for each descriptor, a given Support Vector Machine (SVM) classifier is trained. The set of classifiers is then combined by weighted sum rule. The best result is obtained by combining the extracted descriptors using the following preprocessing methods: wavelet decomposition, local phase quantization, orientation, and the Weber law descriptor. Texture descriptors are extracted from the entire co-occurrence matrix, as well as from sub-windows, and evaluated at multiple scales. We validate our approach on eleven image datasets representing different image classification problems using the Wilcoxon signed rank test. Results show that our approach improves the performance of standard methods. All source code for the approaches tested in this paper will be available at: https://www.dei.unipd.it/node/2357 [ABSTRACT FROM AUTHOR]

Highlights: [•] Cell phenotype image classification by ensemble of descriptors. [•] Compare some recently proposed methods that are based on the co-occurrence matrix. [•] Investigate the correlation among the features that can be extracted from the co-occurrence matrix. [•] Determine the best way to combine co-occurrence matrix based feature sets. [ABSTRACT FROM AUTHOR]