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This paper describes a method of cross-domain object categorization, using the concept of domain adaptation. Here, a classifier is trained using samples from the source/auxiliary domain and performance is observed on a set of test samples taken from a different domain, termed as the target domain. To overcome the difference between the two domains, we aim to find a sequence of optimally weighted sub-spaces,...
Recently Compressive Sensing (CS) has shown great potential in the field of Image processing applications. In this paper we propose a novel CS based technique for simultaneous compression and despeckling of Synthetic Aperture Radar (SAR) images. We incorporate Dual Tree Complex Wavelet Transform (DT-CWT) based denoising within a sparse regularization frame work and solve by a fast projected Landweber...
The present study is an attempt to identify the influence of cirrus cloud on NDVI and AFRI values and to check the scope of replacing NDVI with AFRI in cirrus affected satellite images. Multi-spectral channels of LANDSAT-8 satellite image collected for two different dates of acquisition are used in the current study. Reflectance values of cirrus band are used for correcting red and near infra-red...
This paper presents a new methodology to extract discriminative features from images, that are robust and invariant to image blur and JPEG compression. The local patches are quantized in the polar geometric structure using Log Polar Transformation (LPT). Then two-dimensional Discrete Wavelet Transformation (DWT) is used to decompose the polar structured patch into sub-bands. Each approximation sub-band...
This paper motivates the use of combination of mel frequency cepstral coefficients (MFCC) and its delta derivatives (DMFCC and DDMFCC) calculated using mel spaced Gaussian filter banks for text independent speaker recognition. MFCC modeled on the human auditory system shows robustness against noise and session changes and hence has become synonymous with speaker recognition. Our main aim is to test...
Air quality information has assumed much importance over the years due to the increase in air pollution. One major hindrance in monitoring of air pollutants is the dearth of spatial availability of aerosol concentration measurements due to the cost involved in deployment of sensors. In this respect, self similarity analysis of data can be very useful. This work is based on standard grid based pollutant...
A Non-recursive Motion Similarity Clustering (NMSC) algorithm is proposed to identify pedestrians traveling together in social groups. The clustering algorithm is unsupervised and can automatically identify social groups within a region of interest in a video. Social groups are identified using only pedestrian motion information by imposing motion parameter thresholds defined by social psychological...
In Wireless Body Area Networks (WBAN) the energy consumption is dominated by sensing, processing and communication. Previous Compressed Sensing (CS) based solutions to EEG tele-monitoring over WBAN's could only reduce the communication cost. In this work, we propose to reduce the sensing and processing energy costs as well, by randomly under-sampling the signal. We formulate a theoretically sound...
Key frame based video summarization, which enables an user to access any video in a friendly and meaningful way, has emerged as an important area of research for the multimedia community. Various pattern clustering techniques are applied for the extraction of key frames from a video to form a storyboard. In this work, we improve existing Delaunay graph based video summarization framework with i) semantic...
Automatic extraction of retinal blood vessels is an important issue for the diagnosis and the treatment of different retinal disorders. Most of the retinal images are of low contrast due to non-uniform illumination during acquisition process. Therefore, vessel extraction from unevenly illuminated retinal background is really a challenging task. To extract the vessels which lie in the optic disc region,...
In this work we address the problem of design of an efficient Recommender system based on collaborative filtering framework which achieves improved accuracy with reduced computational complexity and shorter run times. This work is based on representing the low rank constraint as the Ky-Fan norm instead of the commonly employed nuclear norm term. Our formulation uses majorization minimization approach...
In this paper we report our work on multiobjective optimization (MOO) based feature selection approach for event extraction in biomedical texts. Event extraction deals with the detection and classification of expressions that represent complex biological phenomenon involving genes and proteins. We perform feature selection within the framework of a robust machine learning algorithm, namely Conditional...
Speech is the most basic and widely used method for communication. There is a growing need for an expressive speech synthesis especially, when human want to communicate with robots and computers. In this paper, prosody rule-sets are designed to convert neutral to storytelling style speech for Hindi language. In order to generate a storyteller speech from a neutral speech, modification in various prosodic...
This paper describes a method to solve the problem of Object-Centric Content Based Image Retrieval (CBIR), motivated by concepts from theory of cognitive sciences. According to cognitive models, there are two lobes in human brain, one is responsible to solve the problem of object recognition, while the other solves the problem of localization (or detection). It is the exchange of mutual information...
In this paper, we propose a novel approach for human action recognition based on motion capture (MOCAP) information using a Fuzzy convolutional neural network. The MOCAP tracking information of human joints is used to compute the temporal variation of displacement between joints during the execution of an action. Fuzzy membership functions designed to emphasize the discriminative pose associated with...
Recommender systems are designed in such a way that they sort through massive amounts of data so as to help users in finding their preferred items. Currently much research on recommender systems focus on improving the prediction or classification accuracy of the respective algorithms while behavioral aspects are often overlooked. In this paper we focus on a particular behavioral property called monotonicity...
A multilevel thresholding method for the segmentation of Magnetic Resonance (MR) brain images using the concept of intuitionistic fuzzy and rough set is presented here. Intuitionistic fuzzy roughness measure, calculated by considering histogram as lower approximation of rough set and intuitionistic fuzzy histon as upper approximation of rough set, is used to find optimum valley points for segmentation...
This work aims to build scale and rotation invariant features from Non-Subsampled Contourlet Transform (NSCT). The features will have properties similar to the popular Scale Invariant Feature Transform (SIFT). The features will be theoretically (and practically) invariant to scale, location and rotation. We also take care that practically they are invariant to changes in illumination as well. Our...
Automatic annotation of an audio or a music piece with multiple labels helps in understanding the composition of a music. Such meta-level information can be very useful in applications such as music transcription, retrieval, organization and personalization. In this work, we formulate the problem of annotation as multi-label classification which is considerably different from that of a popular single...
The brain Magnetic Resonance (MR) image has an embedded bias field. This field need to be corrected to obtain the actual MR image for classification. In this paper, we have proposed three new schemes to simultaneously estimate the bias field and obtain segmentation. These algorithms are modification of Ahmed et al.'s [4] Bias Corrected FCM (BCFCM) algorithm. The first proposed scheme considers the...
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