The prediction of high-level music categories, such as genres, styles, or personal preferences, helps to organise music collections. The relevance of single audio features for automatic classification depends on a certain category. Relevant feature subsets for each classification task can be identified by means of feature selection. Continuing our previous studies on multi-objective feature sel...

Extraction and utilization of high-level semantic features are critical for more effective video retrieval. However, the performance of video retrieval hasn’t benefited much despite of the advances in high-level feature extraction. To make good use of high-level semantic features in video retrieval, we present a method called pointwise mutual information weighted scheme(PMIWS). The method makes...

Feature-based registration has been popular with a variety of features ranging from voxel intensity to Self-Similarity Context (SSC). In this paper, we examine the question on how features learnt using various Deep Learning (DL) frameworks can be used for deformable registration and whether this feature learning is necessary or not. We investigate the use of features learned by different DL met...

Most of the content-based image retrieval systems focus on similarity-based retrieval of images by utilizing color, shape and texture features. For color-based image retrieval, the average color or color-histograms of images are widely used as feature vectors. In this paper, we propose a new searching scheme, called Fuzzy Membership Value-Indexing, to guarantee higher retrieval quality. This sc...

Low level features like color etc. of an image are really very important for any image retrieval system. This paper implements image classification technique using SURF descriptor and SVM classifier. SURF method which is advanced version of SIFT is used to match feature points of training and test images. SVM classifier based on the outcome of feature points then classifies images. Through the ...

In this paper, we present a transfer learning approach for music classification and regression tasks. We propose to use a pre-trained convnet feature, a concatenated feature vector using the activations of feature maps of multiple layers in a trained convolutional network. We show how this convnet feature can serve as a general-purpose music representation. In the experiments, a convnet is trai...

While there is an enormous amount of music data available, the field of music analysis almost exclusively uses manually designed features. In this work we learn features from music data in a completely unsupervised way and evaluate them on a musical genre classification task. We achieve results very close to state-of-the-art performance which relies on highly hand-tuned feature extractors.

We propose a statistical framework for high-level feature (HLF) extraction, which employs scale-invariant feature transform Gaussian mixture models (SIFT GMMs), acoustic features, and maximal figure-of-merit (MFoM). The MeanInfAP of our best run was 0.1679. Our team placed 11th after all of the runs and 4th among all participating teams. Notably, the InfAPs of “Singing” and “People-dancing” wer...

Motivated by the growing use of multimedia services and the explosion of multimedia collections, efficient retrieval from large-scale multimedia data has become very important in multimedia content analysis and management. In this paper, a novel ranking algorithm is proposed for video retrieval. First, video content is represented by the global and local features and second, multiple correspond...

For the effective operation of intelligent assistive systems working in real-world human environments, it is important to be able to recognise human activities and their intentions. In this paper we propose a novel approach to activity recognition from visual data. Our approach is based on qualitative and quantitative spatio-temporal features which encode the interactions between human subjects...