Low-level Feature Descriptors Research Articles

Image Hashing Through Spatio-triad Relationship

Authenticating the content of the digital image has profound influence in legal matters and in court rooms. Image forensics plays an important role towards it. Proposed approach helps to authenticate the original image by generating a content based image signature that is a unique fingerprint for the image. Our novel approach establishes spatio triad relationship among features and finds the centre of gravity or centroid of the same after indexing. Topology of the triad relationship for the content based low level feature descriptors is preserved through aggregation until single key feature is deduced which is a 128 bit signature hash value and represented in decimal form. Density of feature keypoints influences the centre of gravity which acts as a unique signature for the given image. Manipulated image cannot contribute to restore / regenerate the same signature. We have verified our authentication approach for standard benchmark image dataset like MICC-F220, Columbia Image Splicing Evaluation dataset and Image manipulation dataset from Friedrich Alexander University and have found satisfactory results for the same. Content based image signature obtained is used to verify authenticity of image and for retrieval of video from database. Content based image fingerprint generated can also be considered for embedding as a watermark.

Depth-based saliency estimation for omnidirectional images

Visual saliency estimation aims at identifying and localizing the areas of images and videos that are attractive for a human subject. In this work, a novel approach for estimating the visual saliency in omnidirectional images is proposed. It is based on the identification of low-level image feature descriptors (i.e., the presence of texture, edges, etc.) coupled with the information about the local depth of the scene. To evaluate the performances of the proposed method, the estimated saliency map is compared with the available ground truth through two objective metrics: the correlation coefficient and the Kullback-Leibler divergence. The analysis of the achieved results confirms the validity of the proposed approach.

Sketch-Based 3D Model Retrieval Using Attributes

With the rapid growth of available 3D models on the Internet, how to retrieve 3D models based on hand-drawn sketch retrieval are becoming increasingly important. This article proposes a new sketch-based 3D model retrieval approach. This approach is different from current methods that make use of low-level visual features to capture the search intention of users. The proposed method uses two kinds of semantic attributes, including pre-defined attributes and latent attributes. Specifically, pre-defined attributes are defined manually which can provide prior knowledge about different sketch categories and latent-attributes are more discriminative which can differentiate sketch categories at a finer level. Therefore, these semantic attributes can provide a more descriptive and discriminative meaningful representation than low-level feature descriptors. The experiment results demonstrate that this proposed method can achieve superior performance over previously proposed sketch-based 3D model retrieval methods.

Robust Detection and Affine Rectification of Planar Homogeneous Texture for Scene Understanding

Man-made environments tend to be abundant with planar homogeneous texture, which manifests as regularly repeating scene elements along a plane. In this work, we propose to exploit such structure to facilitate high-level scene understanding. By robustly fitting a texture projection model to optimal dominant frequency estimates in image patches, we arrive at a projective-invariant method to localize such generic, semantically meaningful regions in multi-planar scenes. The recovered projective parameters also allow an affine-ambiguous rectification in real-world images marred with outliers, room clutter, and photometric severities. Comprehensive qualitative and quantitative evaluations are performed that show our method outperforms existing representative work for both rectification and detection. The potential of homogeneous texture for two scene understanding tasks is then explored. Firstly, in environments where vanishing points cannot be reliably detected, or the Manhattan assumption is not satisfied, homogeneous texture detected by the proposed approach is shown to provide alternative cues to obtain a scene geometric layout. Second, low-level feature descriptors extracted upon affine rectification of detected texture are found to be not only class-discriminative but also complementary to features without rectification, improving recognition performance on the 67-category MIT benchmark of indoor scenes. One of our configurations involving deep ConvNet features outperforms most current state-of-the-art work on this dataset, achieving a classification accuracy of 76.90%. The approach is additionally validated on a set of 31 categories (mostly outdoor man-made environments exhibiting regular, repeating structure), being a subset of the large-scale Places2 scene dataset.

Enhanced Iterative Back-Projection Based Super-Resolution Reconstruction of Digital Images

Iterative back-projection (IBP) is a popular and straightforward approach applied successfully in the field of image super-resolution reconstruction (SRR). SRR using IBP (SRR–IBP) methods efficiently satisfies the basic reconstruction constraints. In spatial domain applications, it allows easy inclusion of data and is a computationally efficient method. However, inferior convergence rate, sensitivity to the initial choice of image, the presence of different degrees of ringing artifacts are some of the major disadvantages that limit the performance of SRR–IBP. To relieve these inherent limitations, an evolutionary edge preserving IBP (EEIBP) is proposed in this paper. The proposed work introduces an improved initial choice of the digital image by interpolating the low-resolution digital image via hybridizing the notion of uniform and non-uniform B-spline interpolation. Secondly, it incorporates a spatially adaptive back-projecting kernel (SABPK) and regularization constraints in the iterative process. The SABPK utilizes covariance-based adaptation to restore the lost high-frequency details and is regulated by a control parameter to make the reconstruction process robust. The regularization constraints use different low-level feature descriptors to track the information related to shape and salient visual properties of the digital image. Finally, the overall reconstruction error is minimized via GA, PSO and cuckoo search (CS) algorithms. Experimental results demonstrate the robustness and the effectiveness of the proposed EEIBP method to provide a high-resolution solution with improved visual perception and reduced artifacts. Moreover, EEIBP method optimized via CS algorithm enables a better quality of reconstruction as compared the other search algorithms (gradient, GA and PSO).

Object and Character Recognition Using Spiking Neural Network

Artificial neural network have been well developed so far. First two generation of neural network, have had lot of successful application. Spiking Neural Network (SNN) often referred as third generation of neural network. Which have potential to solve problems related to biological stimulation. In today’s world it is the gaining interest of active research with the areas of neural network. This paper depicts the study about classification and recognition of object and various handwritten characters using one of the popular model of SNN.Leaky integrate and fire model (LIF Model) for object recognition and two level network model for character recognition will be use. In object recognition, after applying image preprocessing operations the image is undergone through various operations for feature extraction. Here energy level of an image which is a low-level image feature and feature descriptor like Extended Histogram of Gradients (ExHoG) and Edge Detection using Extended Epanechnicov Function and Non maxima Repression are used to extract the features from an image. These feature vectors are then given to a classifier to classify an object. In character recognition, Segmentation of document imagesinto text lines and words is an important step for the document understanding however, unlike machine printed documents, the segmentation of the handwritten documents is still considered as the challenging problem due to irregular spacing between words and variation of writing style depending on the person. By developing an algorithm for characters, segmentation, feature extraction of the character and objects for classification of the given characters and object, this problem can be solved. For the evaluation of LIF and two level network model of SNN it is compared with SVM.

Land use and land cover classification for rural residential areas in China using soft-probability cascading of multifeatures

A multifeature soft-probability cascading scheme to solve the problem of land use and land cover (LULC) classification using high-spatial-resolution images to map rural residential areas in China is proposed. The proposed method is used to build midlevel LULC features. Local features are frequently considered as low-level feature descriptors in a midlevel feature learning method. However, spectral and textural features, which are very effective low-level features, are neglected. The acquisition of the dictionary of sparse coding is unsupervised, and this phenomenon reduces the discriminative power of the midlevel feature. Thus, we propose to learn supervised features based on sparse coding, a support vector machine (SVM) classifier, and a conditional random field (CRF) model to utilize the different effective low-level features and improve the discriminability of midlevel feature descriptors. First, three kinds of typical low-level features, namely, dense scale-invariant feature transform, gray-level co-occurrence matrix, and spectral features, are extracted separately. Second, combined with sparse coding and the SVM classifier, the probabilities of the different LULC classes are inferred to build supervised feature descriptors. Finally, the CRF model, which consists of two parts: unary potential and pairwise potential, is employed to construct an LULC classification map. Experimental results show that the proposed classification scheme can achieve impressive performance when the total accuracy reached about 87%.

There's More to Groove than Bass in Electronic Dance Music: Why Some People Won't Dance to Techno.

The purpose of this study was to explore the relationship between audio descriptors for groove-based electronic dance music (EDM) and raters’ perceived cognitive, affective, and psychomotor responses. From 198 musical excerpts (length: 15 sec.) representing 11 subgenres of EDM, 19 low-level audio feature descriptors were extracted. A principal component analysis of the feature vectors indicated that the musical excerpts could effectively be classified using five complex measures, describing the rhythmical properties of: (a) the high-frequency band, (b) the mid-frequency band, and (c) the low-frequency band, as well as overall fluctuations in (d) dynamics, and (e) timbres. Using these five complex audio measures, four meaningful clusters of the EDM excerpts emerged with distinct musical attributes comprising music with: (a) isochronous bass and static timbres, (b) isochronous bass with fluctuating dynamics and rhythmical variations in the mid-frequency range, (c) non-isochronous bass and fluctuating timbres, and (d) non-isochronous bass with rhythmical variations in the high frequencies. Raters (N = 99) were each asked to respond to four musical excerpts using a four point Likert-Type scale consisting of items representing cognitive (n = 9), affective (n = 9), and psychomotor (n = 3) domains. Musical excerpts falling under the cluster of “non-isochronous bass with rhythmical variations in the high frequencies” demonstrated the overall highest composite scores as evaluated by the raters. Musical samples falling under the cluster of “isochronous bass with static timbres” demonstrated the overall lowest composite scores as evaluated by the raters. Moreover, music preference was shown to significantly affect the systematic patterning of raters’ responses for those with a musical preference for “contemporary” music, “sophisticated” music, and “intense” music.

High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder

An unsupervised feature learning framework based on auto-encoder is proposed to learn sparse feature representations for remote-sensing imagery retrieval in this letter. The low-level feature descriptors are extracted and exploited to learn a set of feature extractors, which are then used to encode the low-level feature descriptors to generate new sparse features. The learned feature representations are applied to aerial images randomly selected from the University of California Merced data set. The results indicate that the performance of our proposed framework is comparable or superior to that of the state-of-the-art method. The framework is proved to be an effective approach to manage the huge volume of remote-sensing data and to retrieve the desired remote-sensing imagery.

Face Spoofing Detection Through Visual Codebooks of Spectral Temporal Cubes.

Despite important recent advances, the vulnerability of biometric systems to spoofing attacks is still an open problem. Spoof attacks occur when impostor users present synthetic biometric samples of a valid user to the biometric system seeking to deceive it. Considering the case of face biometrics, a spoofing attack consists in presenting a fake sample (e.g., photograph, digital video, or even a 3D mask) to the acquisition sensor with the facial information of a valid user. In this paper, we introduce a low cost and software-based method for detecting spoofing attempts in face recognition systems. Our hypothesis is that during acquisition, there will be inevitable artifacts left behind in the recaptured biometric samples allowing us to create a discriminative signature of the video generated by the biometric sensor. To characterize these artifacts, we extract time-spectral feature descriptors from the video, which can be understood as a low-level feature descriptor that gathers temporal and spectral information across the biometric sample and use the visual codebook concept to find mid-level feature descriptors computed from the low-level ones. Such descriptors are more robust for detecting several kinds of attacks than the low-level ones. The experimental results show the effectiveness of the proposed method for detecting different types of attacks in a variety of scenarios and data sets, including photos, videos, and 3D masks.

Exploiting stereoscopic disparity for augmenting human activity recognition performance

This work investigates several ways to exploit scene depth information, implicitly available through the modality of stereoscopic disparity in 3D videos, with the purpose of augmenting performance in the problem of recognizing complex human activities in natural settings. The standard state-of-the-art activity recognition algorithmic pipeline consists in the consecutive stages of video description, video representation and video classification. Multimodal, depth-aware modifications to standard methods are being proposed and studied, both for video description and for video representation, that indirectly incorporate scene geometry information derived from stereo disparity. At the description level, this is made possible by suitably manipulating video interest points based on disparity data. At the representation level, the followed approach represents each video by multiple vectors corresponding to different disparity zones, resulting in multiple activity descriptions defined by disparity characteristics. In both cases, a scene segmentation is thus implicitly implemented, based on the distance of each imaged object from the camera during video acquisition. The investigated approaches are flexible and able to cooperate with any monocular low-level feature descriptor. They are evaluated using a publicly available activity recognition dataset of unconstrained stereoscopic 3D videos, consisting in extracts from Hollywood movies, and compared both against competing depth-aware approaches and a state-of-the-art monocular algorithm. Quantitative evaluation reveals that some of the examined approaches achieve state-of-the-art performance.

Anomaly detection based on local nearest neighbor distance descriptor in crowded scenes.

We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance.

Unsupervised Learning for Satellite Image Classification

A new method of classifying satellite images into different categories such as forest, desert, river etc., with the help of Support Vector Machine (SVM) and unsupervised learning method using MATLAB is presented. Dense low-level feature descriptors are extracted and exploited in a novel way to learn a set of basis functions. The low-level feature descriptors are encoded in terms of the basis functions to generate new sparse representation for the feature descriptors. We pool these sparse features by simple averaging and show that this method will help us to choose SVM classifier which is having less cost. Experimental results show its good performance, when tested with different saliency criteria, such as accuracy, less computational time, and the combination of both.

Unsupervised Feature Learning for Aerial Scene Classification

The rich data provided by high-resolution satellite imagery allow us to directly model aerial scenes by understanding their spatial and structural patterns. While pixel- and object-based classification approaches are widely used for satellite image analysis, often these approaches exploit the high-fidelity image data in a limited way. In this paper, we explore an unsupervised feature learning approach for scene classification. Dense low-level feature descriptors are extracted to characterize the local spatial patterns. These unlabeled feature measurements are exploited in a novel way to learn a set of basis functions. The low-level feature descriptors are encoded in terms of the basis functions to generate new sparse representation for the feature descriptors. We show that the statistics generated from the sparse features characterize the scene well producing excellent classification accuracy. We apply our technique to several challenging aerial scene data sets: ORNL-I data set consisting of 1-m spatial resolution satellite imagery with diverse sensor and scene characteristics representing five land-use categories, UCMERCED data set representing twenty one different aerial scene categories with sub-meter resolution, and ORNL-II data set for large-facility scene detection. Our results are highly promising and, on the UCMERCED data set we outperform the previous best results. We demonstrate that the proposed aerial scene classification method can be highly effective in developing a detection system that can be used to automatically scan large-scale high-resolution satellite imagery for detecting large facilities such as a shopping mall.

Face spoofing detection from single images using texture and local shape analysis

Current face biometric systems are vulnerable to spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterisation of printing artefacts and differences in light reflection, the authors propose to approach the problem of spoofing detection from texture analysis point of view. Indeed, face prints usually contain printing quality defects that can be well detected using texture and local shape features. Hence, the authors present a novel approach based on analysing facial image for detecting whether there is a live person in front of the camera or a face print. The proposed approach analyses the texture and gradient structures of the facial images using a set of low-level feature descriptors, fast linear classification scheme and score level fusion. Compared to many previous works, the authors proposed approach is robust and does not require user-cooperation. In addition, the texture features that are used for spoofing detection can also be used for face recognition. This provides a unique feature space for coupling spoofing detection and face recognition. Extensive experimental analysis on three publicly available databases showed excellent results compared to existing works.

Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion

We present a content-based image retrieval framework for diverse collections of medical images of different modalities, anatomical regions, acquisition views, and biological systems. For the image representation, the probabilistic output from multi-class support vector machines (SVMs) with low-level features as inputs are represented as a vector of confidence or membership scores of pre-defined image categories. The outputs are combined for feature-level fusion and retrieval based on the combination rules that are derived by following Bayes’ theorem. We also propose an adaptive similarity fusion approach based on a linear combination of individual feature level similarities. The feature weights are calculated by considering both the precision and the rank order information of top retrieved relevant images as predicted by SVMs. The weights are dynamically updated by the system for each individual search to produce effective results. The experiments and analysis of the results are based on a diverse medical image collection of 11,000 images of 116 categories. The performances of the classification and retrieval algorithms are evaluated both in terms of error rate and precision–recall. Our results demonstrate the effectiveness of the proposed framework as compared to the commonly used approaches based on low-level feature descriptors.

MECCA: Hypermedia Capturing of Collaborative Scientific Discourses about Movies

Introduction Foucault defines 'discourse' as the medium by which ideas are exchanged (Wikipedia, 2004). Scholarly communication in the Humanities and the Cultural Studies heavily depend on the discursive nature of knowledge creation. changes over the centuries altered not only the communicational culture of scholars regarding their archives and text production strategies but also the communication situation in society, thus causing scientific, artistic, and societal paradigm shifts. The DFG-funded interdisciplinary Collaborative Research Center Media and Cultural Communication was founded in 1999 to study the nature and impact of media use and change on the discourses in cultural and scientific communication. Supported by historical analysis of media use, our project within this Center intends to create new workplaces for scientists in the 21st century. These interdisciplinary workplaces will influence the way scientists organize knowledge and cooperation within networked hypermedia information systems. Research and results will also have impact on the organization of social, economic and politic knowledge organization and cooperation patterns like the organization of public debates. Supporting communication and knowledge creation in digitally networked cultural science communities represents a specific challenge for the development and organizational structure of hypermedia information systems. Several years of cooperation with scientists from different disciplines in the humanities and the cultural studies have revealed at least three main requirements: 1. Semantic Freedom: The discursive nature of knowledge creation in the humanities, based on an intense exposure to hypermedia artifacts and underlying theories, requires support of different digital media to be combined with (almost) arbitrary metadata which characterize the situational background of an artifact. These digital artifacts are reifications of actual discourses and have special semantics for each user and each community of practice (Wenger, 1998). The situational backgrounds belonging to an artifact can be quite different depending on the scholar, her role in the community and her educational background. Concepts for dynamic hypermedia context management with a high degree of semantic freedom are thus needed for scholarly communications in the humanities. 2. Openness of repositories: This is an indispensable prerequisite for scaling such systems beyond the purposes of one community of practice. Scientific discourses in an informed society could not be isolated in the ivory tower anymore. Interdisciplinary and networked work settings lead to more exchange within the scientific community as well with society, industry and the political system. Modern information system should allow scholars of any kind to check out and modify archives, and bring them back into the discussion, again fostering discourses by simultaneously assuring intellectual property. We are aware that this is not only a requirement of a class of systems but is accompanied by the transformation of the humanities themselves. 3. Fluidity of archives: A naive multimedia understanding--e.g. that hypermedia can transport knowledge--is insufficient, since complex interrelations between media exist as well as complex cultural interfaces to these media (Manovich, 2001). Multimedia repositories are a big advantage but the contextualized and meaningful management of hypermedia artifacts needs more advanced hypermedia management strategies (fluid archives) than the usual basic support for storage and retrieval on the basis of low-level feature descriptors implementing old-fashioned de-contextualization strategies propagated in state-of-the-art information systems for scholarly communications. In the remainder of this paper, we discuss related literature first. We introduce a theoretical reflection on knowledge creation processes in the humanities related to the use of different media. …