Low-level Content Features Research Articles

RETRACTED ARTICLE: Parallel deep convolutional neural network for content based medical image retrieval

DICOM images which helps in diagnosis and prognosis would be critical component in health care systems. Speedy recovery of past historic DICOM images based on the given query image is becoming a critical requirement for the Laboratories and Doctors for quick inference and accurate analogy of the patient conditions. In existing, It is also identified that there is a presence of imbalanced data set which degrade the retrieval accuracy of the model which may reduce by using extract the different kinds of features. The DCNN classifiers are trained by datasets whose data distributions of individual classes are not even or similar, they have always suffered from imbalanced classification performance against classes. Through DCNN can be used to minimize the gaps in terms of accuracy and retrieval but still efficiency parallelization would be essential for faster training and retrieval time. Time complexity is always been a major issue in DCNN, to overcome the above complexity the parallelization of model or data dimension need to be adapted. In this paper, parallel deep convolutional neural network (PDCNN) model is proposed by hyper parameter optimimzation for CBMIR system. The proposed model incorporating the low level content features, high level semantic features and compact features along with DCNN features to tackle the imbalanced dataset problem and reducing the DCNN training time for DICOM images. The high-level and compact features are extracted to resolve the imbalanced dataset problem by using the following algorithms: (a) local binary pattern (LBP), (b) histogram of oriented gradients (HOG) and (c) radon. The data parallelism was adopted in the proposed DCNN model to reduce the network training time by execution of DCNN layers across multiple CPU cores on a single PC. The implementation results for the proposed model in terms of Precision, Recall and F measure values are 87%, 87% and 92% respectively.

A quantum-inspired multimodal sentiment analysis framework

Multimodal sentiment analysis aims to capture diversified sentiment information implied in data that are of different modalities (e.g., an image that is associated with a textual description or a set of textual labels). The key challenge is rooted on the “semantic gap” between different low-level content features and high-level semantic information. Existing approaches generally utilize a combination of multimodal features in a somehow heuristic way. However, how to employ and combine multiple information from different sources effectively is still an important yet largely unsolved problem. To address the problem, in this paper, we propose a Quantum-inspired Multimodal Sentiment Analysis (QMSA) framework. The framework consists of a Quantum-inspired Multimodal Representation (QMR) model (which aims to fill the “semantic gap” and model the correlations between different modalities via density matrix), and a Multimodal decision Fusion strategy inspired by Quantum Interference (QIMF) in the double-slit experiment (in which the sentiment label is analogous to a photon, and the data modalities are analogous to slits). Extensive experiments are conducted on two large scale datasets, which are collected from the Getty Images and Flickr photo sharing platform. The experimental results show that our approach significantly outperforms a wide range of baselines and state-of-the-art methods.

A 3D Engineering Model Retrieval Algorithm Based on Relevance Feedback and Features Combination

In order to reuse 3D models and design knowledge efficiently, a number of 3D model retrieval algorithms based on content features of models have been proposed in recent years. Although, the features-based methods have achieved some progress, there are two limitations stilly. The first, single content feature cant be suit for all kinds of 3D models; different features have different strengths and weakness. The second, semantic gap, the semantic of model is independent from low-level characteristics. For those two issues, we present a 3D engineering model retrieval algorithm based on relevance feedback and features combination in this paper. The proposed method takes advantage of multiple features by allying them with weights. In the retrieval process, our method utilizes the Particle Swarm Optimization to update the weights dynamically based on users relevance feedback information in order to narrowing the gap between high-level semantic knowledge and low-level content features. The Experiments, based on publicly available 3D model database Engineering Shape Benchmark (ESB) developed by Purdue University, suggested that the proposed approach has better retrieval ability than traditional ones.

Home Video Visual Quality Assessment With Spatiotemporal Factors

Compared with the video programs taken by professionals, home videos are always with low quality content resulted from non-professional capture skills. In this paper, we present a novel spatiotemporal quality assessment scheme in terms of low-level content features for home videos. In contrast to existing frame-level-based quality assessment approaches, a type of temporal segment of video, subshot, is selected as the basic unit for quality assessment. A set of spatiotemporal visual artifacts, regarded as the key factors affecting the overall perceived quality (i.e., unstableness and jerkiness as temporal factors; infidelity, blurring, brightness, and orientation as spatial factors), are mined from each subshot based on particular characteristics of home videos. The relationship between the overall quality metric and these factors are exploited by three different methods, including user study-based, rule-based and learning-based. To validate the proposed scheme, we present a scalable quality-based home video summarization system from a novel perspective-achieving the best visual quality while simultaneously preserving the most informative content. A comparison user study between this system and the attention model-based video skimming approach demonstrated the effectiveness of the proposed quality assessment scheme

Classification-based multidimensional adaptation prediction for scalable video coding using subjective quality evaluation

Scalable video coding offers a flexible representation for video adaptation in multiple dimensions comprising spatial detail and temporal resolution, thus providing great benefits for universal media access (UMA) applications. However, currently most of the approaches address the multidimensional adaptation (MDA) problem in an ad hoc manner. One challenging issue affecting the systematic MDA solution is the difficulty in constructing analytical models in theoretical optimization that capture the relations between video utility and MDA operations. In this paper, we propose a general classification-based prediction framework for selecting the preferred MDA operations based on subjective quality evaluation. For this purpose, we first apply domain-specific knowledge or general unsupervised clustering to construct distinct categories within which the videos share similar preferred MDA operations. Thereafter, a machine learning based method is applied where the low level content features extracted from the compressed video streams are employed to train a framework for the problem of joint signal-to-noise ratio (SNR)-temporal adaptation selection based on the motion compensated three-dimensional subband coding (MC-3DSBC) system. We conduct extensive subjective tests involving 31 subjects, 128 video clips, and formal subjective quality metrics. Statistical analysis of the experimental results confirms the excellent accuracy in using domain knowledge and content features to predict the MDA operation.

Detecting image orientation based on low-level visual content

Accurately and automatically detecting image orientation is of great importance in intelligent image processing. In this paper, we present automatic image orientation detection algorithms based on both the luminance (structural) and chrominance (color) low-level content features. The statistical learning support vector machines (SVMs) are used in our approach as the classifiers. The different sources of the extracted image features, as well as the binary classification nature of SVM, require our system to be able to integrate the outputs from multiple classifiers. Both static combiner (averaging) and trainable combiner (also based on SVMs) are proposed and evaluated in this work. Furthermore, two rejection options (regular and re-enforced ambiguity rejections) are employed to improve orientation detection accuracy by sieving out images with low confidence values during the classification. Large amounts of experiments have been conducted on a database of more than 14,000 images to validate our approaches. Discussions and future directions for this work are also addressed at the end of the paper.