Learning a Representative and Discriminative Part Model with Deep Convolutional Features for Scene Recognition
نویسندگان
چکیده
The discovery of key and distinctive parts is critical for scene parsing and understanding. However, it is a challenging problem due to the weakly supervised condition, i.e., no annotation for parts is available. To address above issues, we propose a unified framework for learning a representative and discriminative part model with deep convolutional features. Firstly, we employ selective search method to generate regions that are more likely to be centered around the distinctive parts, which is used as parts training set. Then, the feature of each part region is extracted by forward propagating it into the Convolutional Neural Network (CNN). The CNN network is pre-trained by the large auxiliary ImageNet dataset and then fine-tuned on the particular scene images. To learn the parts model, we build a mid-level part dictionary based on sparse coding with a discriminative regularization. The two terms, i.e., the sparse reconstruction error term and the label consistent term, indicate the representative and discriminative properties respectively. Finally, we apply the learned parts model to build image-level representation for the scene recognition task. Extensive experiments demonstrate that we achieve state-of-the-art performances on the standard scene benchmarks, i.e. Scene-15 and MIT Indoor-67.
منابع مشابه
An Information-Theoretic Discussion of Convolutional Bottleneck Features for Robust Speech Recognition
Convolutional Neural Networks (CNNs) have been shown their performance in speech recognition systems for extracting features, and also acoustic modeling. In addition, CNNs have been used for robust speech recognition and competitive results have been reported. Convolutive Bottleneck Network (CBN) is a kind of CNNs which has a bottleneck layer among its fully connected layers. The bottleneck fea...
متن کاملEMG-based wrist gesture recognition using a convolutional neural network
Background: Deep learning has revolutionized artificial intelligence and has transformed many fields. It allows processing high-dimensional data (such as signals or images) without the need for feature engineering. The aim of this research is to develop a deep learning-based system to decode motor intent from electromyogram (EMG) signals. Methods: A myoelectric system based on convolutional ne...
متن کاملA hybrid EEG-based emotion recognition approach using Wavelet Convolutional Neural Networks (WCNN) and support vector machine
Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool which makes processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate. In this paper, a hybrid approach based on deep features extracted from Wave...
متن کاملA Deep-Local-Global Feature Fusion Framework for High Spatial Resolution Imagery Scene Classification
High spatial resolution (HSR) imagery scene classification has recently attracted increased attention. The bag-of-visual-words (BoVW) model is an effective method for scene classification. However, it can only extract handcrafted features, and it disregards the spatial layout information, whereas deep learning can automatically mine the intrinsic features as well as preserve the spatial locatio...
متن کاملSpeech Emotion Recognition Using Scalogram Based Deep Structure
Speech Emotion Recognition (SER) is an important part of speech-based Human-Computer Interface (HCI) applications. Previous SER methods rely on the extraction of features and training an appropriate classifier. However, most of those features can be affected by emotionally irrelevant factors such as gender, speaking styles and environment. Here, an SER method has been proposed based on a concat...
متن کامل