Integrating Color and Spatial Feature for Content-Based Image Retrieval

In this paper, we present a novel and efficient scheme for extracting, indexing and retrieving color images. Our motivation was to reduce the space overhead of partition-based approaches taking advantage of the fact that only a relatively low number of distinct values of a particular visual feature is present in most images. To extract color feature and build indices into our image database we take into consideration factors such as human color perception and perceptual range, and the image is partitioned into a set of regions by using a simple classifying scheme. The compact color feature vector and the spatial color histogram, which are extracted from the seqmented image region, are used for representing the color and spatial information in the image. We have also developed the region-based distance measures to compare the similarity of two images. Extensive tests on a large image collection were conducted to demonstrate the effectiveness of the proposed approach.

Deep learning-based method for detecting anomalies in electromagnetic environment situation

The anomaly detection of electromagnetic environment situation(EMES) has essential reference value for electromagnetic equipment behavior cognition and battlefield threat assessment.In this paper,we proposed a deep learning-based method for detecting anomalies in EMES to address the problem of relatively low efficiency of electromagnetic environment situation anomaly detection(EMES-AD).Firstly,the convolutional kernel extracts the static features of different regions of the EMES.Secondly,the dynamic features of the region are obtained by using a recurrent neural network(LSTM).Thirdly,the Spatio-temporal features of the region are recovered by using a de-convolutional network and then fused to predict the EMES.The structural similarity algorithm(SSIM) is used to determine whether it is anomalous.We developed the detection framework,de-signed the network parameters,simulated the data sets containing different anomalous types of EMES,and carried out the detection experiments.The experimental results show that the proposed method is effective.

Using an integrated feature set to generalize and justify the Chinese-to-English transferring rule of the 'ZHE' aspect

In machine translation(MT) practice,there is an urgent need for constructing a set of Chinese-to-English aspect transferring rules to define the transferring conditions.The integrated feature set was used to generalize and justify the Chinese-to-English transferring rule of the 'ZHE' aspect(ZHE Rule).A ZHE classification model was built in this study.The impacts of each set of temporal,lexical aspectual,and syntactic features,and their integrated impacts,on the accuracy of the ZHE Rule were tested.Over 600 misclassified corpus sentences were manually examined.A 10-fold cross-validation was used with a decision tree algorithm.The main results are:(1) The ZHE Rule was generalized and justified to have a higher accuracy under the two metrics:the precision rate and the areas under the receiver operating characteristic curve(AUC).(2) The temporal,lexical aspectual,and syntactic feature sets have an integrated contribution to the accuracy of the ZHE Rule.The syntactic and temporal features have an impact on ZHE aspect derivations,while the lexical aspectual features are not predictive of ZHE aspect derivation.(3) While associated with active verbs,the ZHE aspect can denote a perfective situation.This study suggests that the temporal and syntactic features are the predictive ZHE aspect classification features and that the ZHE Rule with an overall precision rate of 80.1% is accurate enough to be further explored in MT practice.The machine learning method,decision tree,can be applied to the automatic aspect transferring in MT research and aspectual interpretations in linguistic research.

Novel Algorithm to Suppress Random Pulse Interference in Spikes

Spikes detection and sorting play an important role in study of neural information coding.Spikes were generally obtained by threshold detection after filtered in traditional detection,which failed to suppress the random pulse interference(RPI),In this paper,a novel algorithm was provided to suppress RPI using integrated feature.The raw neural signals from the primary visual cortex in rats were detected with microelectrode array.After the feature differences between spikes and RPls were compared,the features which include waveform and non-waveform features were extracted respectively,and then the integrated feature was established based on Fisher's discrimi nant ratio to separate between spikes and RPls.The test results of simulation and experiment show that the separability capability of the integrated feature is nearly two times greater than the individual feature,the average correct recognition rate of spikes and RPls is over 93%,and the detection rate of spike is effectively improved.