The task of classifying videos of natural dynamic scenes into appropriate classes has gained lot of attention in recent years. The problem especially becomes challenging when the camera used to capture the video is dynamic.In this paper, we propose a statistical aggregation (SA) solution based on convolutional neural networks (CNNs) to address this problem. We call our approach as SA-CNN. The a...

Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. We propose a datadriven face analysis approach that is not only capable of extracting features relevant to a given face analysis task, but is also robust with regard to face location ch...

In this paper, we attempt to tackle the MediaEval 2014 Retrieving Diverse Social Images challenge, a filter and refinement problem defined for a Flickr-based ranked set of social images. We build upon solutions proposed in [5] and mainly focus on exploiting the joint use of all modalities. The use of image features extracted from a deep convolutional neural network, combined with the use of dis...

Lung nodule detection is a class imbalanced problem because nodules are found with much lower frequency than nonnodules. In the class imbalanced problem, conventional classifiers tend to be overwhelmed by the majority class and ignore the minority class. We therefore propose cascaded convolutional neural networks to cope with the class imbalanced problem. In the proposed approach, cascaded conv...

A trademark is a mark used to identif​y various commodities. If same or similar trademark is registered for the same or similar commodity, the purchaser of the goods may be confused. Therefore, in the process of trademark registration examination, the examiner judges whether the trademark is the same or similar to the other applied or registered trademarks. ​The confusion in trademarks is ba...

This paper proposes a new framework for semantic segmentation of objects in videos. We address the label inconsistency problem of deep convolutional neural networks (DCNNs) by exploiting the fact that videos have multiple frames; in a few frames the object is confidently-estimated (CE) and we use the information in them to improve labels of the other frames. Given the semantic segmentation resu...

Alzheimer’s Disease destroys brain cells causing people to lose their memory, mental functions and ability to continue daily activities. It is a severe neurological brain disorder which is not curable, but earlier detection of Alzheimer’s Disease can help for proper treatment and to prevent brain tissue damage. Detection and classification of Alzheimer’s Disease (AD) is challenging because some...

In this paper, we present a new automatic diagnosis method of facial acne vulgaris based on convolutional neural network. This method is proposed to overcome the shortcoming of classification types in previous methods. The core of our method is to extract features of images based on convolutional neural network and achieve classification by classifier. We design a binary classifier of skin-and-...

We explore using Convolutional Neural Networks (CNNs) for a small-footprint keyword spotting (KWS) task. CNNs are attractive for KWS since they have been shown to outperform DNNs with far fewer parameters. We consider two different applications in our work, one where we limit the number of multiplications of the KWS system, and another where we limit the number of parameters. We present new CNN...

Convolutional Neural Network demonstrates high performance on ImageNet Large-Scale Visual Recognition Challenges contest. Nevertheless, the publish results only show overall performance for all images classes. There is no further analysis for what special images get worse results and how they could be improved. In this paper, we provide deep performance analysis base on different types of image...