Fusing image representations for classification using support vector machines

In order to improve classification accuracy different image representations are usually combined. This can be done by using two different fusing schemes. In feature level fusion schemes, image representations are combined before the classification process. In classifier fusion, the decisions taken separately based on individual representations are fused to make a decision. In this paper the main methods derived for both strategies are evaluated. Our experimental results show that classifier fusion performs better. Specifically Bayes belief integration is the best performing strategy for image classification task.Comment: Image and Vision Computing New Zealand, 2009. IVCNZ '09. 24th International Conference, Wellington : Nouvelle-Z\'elande (2009

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Evaluating Two-Stream CNN for Video Classification

Videos contain very rich semantic information. Traditional hand-crafted features are known to be inadequate in analyzing complex video semantics. Inspired by the huge success of the deep learning methods in analyzing image, audio and text data, significant efforts are recently being devoted to the design of deep nets for video analytics. Among the many practical needs, classifying videos (or video clips) based on their major semantic categories (e.g., "skiing") is useful in many applications. In this paper, we conduct an in-depth study to investigate important implementation options that may affect the performance of deep nets on video classification. Our evaluations are conducted on top of a recent two-stream convolutional neural network (CNN) pipeline, which uses both static frames and motion optical flows, and has demonstrated competitive performance against the state-of-the-art methods. In order to gain insights and to arrive at a practical guideline, many important options are studied, including network architectures, model fusion, learning parameters and the final prediction methods. Based on the evaluations, very competitive results are attained on two popular video classification benchmarks. We hope that the discussions and conclusions from this work can help researchers in related fields to quickly set up a good basis for further investigations along this very promising direction.Comment: ACM ICMR'1

Speech Mode Classification using the Fusion of CNNs and LSTM Networks

Speech mode classification is an area that has not been as widely explored in the field of sound classification as others such as environmental sounds, music genre, and speaker identification. But what is speech mode? While mode is defined as the way or the manner in which something occurs or is expressed or done, speech mode is defined as the style in which the speech is delivered by a person. There are some reports on speech mode classification using conventional methods, such as whispering and talking using a normal phonetic sound. However, to the best of our knowledge, deep learning-based methods have not been reported in the open literature for the aforementioned classification scenario. Specifically, in this work we assess the performance of image-based classification algorithms on this challenging speech mode classification problem, including the usage of pre-trained deep neural networks, namely AlexNet, ResNet18 and SqueezeNet. Thus, we compare the classification efficiency of a set of deep learning-based classifiers, while we also assess the impact of different 2D image representations (spectrograms, mel-spectrograms, and their image-based fusion) on classification accuracy. These representations are used as input to the networks after being generated from the original audio signals. Next, we compare the accuracy of the DL-based classifies to a set of machine learning (ML) ones that use as their inputs Mel-Frequency Cepstral Coefficients (MFCCs) features. Then, after determining the most efficient sampling rate for our classification problem (i.e. 32kHz), we study the performance of our proposed method of combining CNN with LSTM (Long Short-Term Memory) networks. For this purpose, we use the features extracted from the deep networks of the previous step. We conclude our study by evaluating the role of sampling rates on classification accuracy by generating two sets of 2D image representations – one with 32kHz and the other with 16kHz sampling. Experimental results show that after cross validation the accuracy of DL-based approaches is 15% higher than ML ones, with SqueezeNet yielding an accuracy of more than 91% at 32kHz, whether we use transfer learning, feature-level fusion or score-level fusion (92.5%). Our proposed method using LSTMs further increased that accuracy by more than 3%, resulting in an average accuracy of 95.7%