Log in

Relevant bibliographies by topics / FEATURE ENCODING / Journal articles

To see the other types of publications on this topic, follow the link: FEATURE ENCODING.

Journal articles on the topic 'FEATURE ENCODING'

Author: Grafiati

Published: 11 September 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'FEATURE ENCODING.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Lathroum, Amanda. "Feature encoding by neural nets." Phonology 6, no. 2 (1989): 305–16. http://dx.doi.org/10.1017/s0952675700001044.

Full text

Abstract:

While the use of categorical features seems to be the appropriate way to express sound patterns within languages, these features do not seem adequate to describe the sounds actually produced by speakers. Examination of the speech signal fails to reveal objective, discrete phonological segments. Similarly, segments are not directly observable in the flow of articulatory movements, and vary slightly according to an individual speaker's articulatory strategies. Because of the lack of a reliable relationship between segments and speech sounds, a plausible transition from feature representation to

APA, Harvard, Vancouver, ISO, and other styles

2

Jaswal, Snehlata, and Robert H. Logie. "Configural encoding in visual feature binding." Journal of Cognitive Psychology 23, no. 5 (2011): 586–603. http://dx.doi.org/10.1080/20445911.2011.570256.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Wu, Pengxiang, Chao Chen, Jingru Yi, and Dimitris Metaxas. "Point Cloud Processing via Recurrent Set Encoding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5441–49. http://dx.doi.org/10.1609/aaai.v33i01.33015441.

Full text

Abstract:

We present a new permutation-invariant network for 3D point cloud processing. Our network is composed of a recurrent set encoder and a convolutional feature aggregator. Given an unordered point set, the encoder firstly partitions its ambient space into parallel beams. Points within each beam are then modeled as a sequence and encoded into subregional geometric features by a shared recurrent neural network (RNN). The spatial layout of the beams is regular, and this allows the beam features to be further fed into an efficient 2D convolutional neural network (CNN) for hierarchical feature aggrega

APA, Harvard, Vancouver, ISO, and other styles

4

Eurich, Christian W., and Stefan D. Wilke. "Multidimensional Encoding Strategy of Spiking Neurons." Neural Computation 12, no. 7 (2000): 1519–29. http://dx.doi.org/10.1162/089976600300015240.

Full text

Abstract:

Neural responses in sensory systems are typically triggered by a multitude of stimulus features. Using information theory, we study the encoding accuracy of a population of stochastically spiking neurons characterized by different tuning widths for the different features. The optimal encoding strategy for representing one feature most accurately consists of narrow tuning in the dimension to be encoded, to increase the single-neuron Fisher information, and broad tuning in all other dimensions, to increase the number of active neurons. Extremely narrow tuning without sufficient receptive field o

APA, Harvard, Vancouver, ISO, and other styles

5

Shinomiya, Yuki, and Yukinobu Hoshino. "A Quantitative Quality Measurement for Codebook in Feature Encoding Strategies." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 7 (2017): 1232–39. http://dx.doi.org/10.20965/jaciii.2017.p1232.

Full text

Abstract:

Nowadays, a feature encoding strategy is a general approach to represent a document, an image or audio as a feature vector. In image recognition problems, this approach treats an image as a set of partial feature descriptors. The set is then converted to a feature vector based on basis vectors called codebook. This paper focuses on a prior probability, which is one of codebook parameters and analyzes dependency for the feature encoding. In this paper, we conducted the following two experiments, analysis of prior probabilities in state-of-the-art encodings and control of prior probabilities. Th

APA, Harvard, Vancouver, ISO, and other styles

6

Ronran, Chirawan, Seungwoo Lee, and Hong Jun Jang. "Delayed Combination of Feature Embedding in Bidirectional LSTM CRF for NER." Applied Sciences 10, no. 21 (2020): 7557. http://dx.doi.org/10.3390/app10217557.

Full text

Abstract:

Named Entity Recognition (NER) plays a vital role in natural language processing (NLP). Currently, deep neural network models have achieved significant success in NER. Recent advances in NER systems have introduced various feature selections to identify appropriate representations and handle Out-Of-the-Vocabulary (OOV) words. After selecting the features, they are all concatenated at the embedding layer before being fed into a model to label the input sequences. However, when concatenating the features, information collisions may occur and this would cause the limitation or degradation of the

APA, Harvard, Vancouver, ISO, and other styles

7

James, Melissa S., Stuart J. Johnstone, and William G. Hayward. "Event-Related Potentials, Configural Encoding, and Feature-Based Encoding in Face Recognition." Journal of Psychophysiology 15, no. 4 (2001): 275–85. http://dx.doi.org/10.1027//0269-8803.15.4.275.

Full text

Abstract:

Abstract The effects of manipulating configural and feature information on the face recognition process were investigated by recording event-related potentials (ERPs) from five electrode sites (Fz, Cz, Pz, T5, T6), while 17 European subjects performed an own-race and other-race face recognition task. A series of upright faces were presented in a study phase, followed by a test phase where subjects indicated whether inverted and upright faces were studied or novel via a button press response. An inversion effect, illustrating the disruption of upright configural information, was reflected in ac

APA, Harvard, Vancouver, ISO, and other styles

8

S RAO, VIBHA, and P. RAMESH NAIDU. "Periocular and Iris Feature Encoding - A Survey." International Journal of Innovative Research in Computer and Communication Engineering 03, no. 01 (2015): 368–74. http://dx.doi.org/10.15680/ijircce.2015.0301023.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

HUO, Lu, and Leijie ZHANG. "Combined feature compression encoding in image retrieval." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 27, no. 3 (2019): 1603–18. http://dx.doi.org/10.3906/elk-1803-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Lee, Hui-Jin, Ki-Sang Hong, Henry Kang, and Seungyong Lee. "Photo Aesthetics Analysis via DCNN Feature Encoding." IEEE Transactions on Multimedia 19, no. 8 (2017): 1921–32. http://dx.doi.org/10.1109/tmm.2017.2687759.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Huo, Lu, Tianrong Rao, and Leijie Zhang. "Fused feature encoding in convolutional neural network." Multimedia Tools and Applications 78, no. 2 (2018): 1635–48. http://dx.doi.org/10.1007/s11042-018-6249-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Li, Cuixia, Shanshan Yang, Li Shi, Yue Liu, and Yinghao Li. "PTRNet: Global Feature and Local Feature Encoding for Point Cloud Registration." Applied Sciences 12, no. 3 (2022): 1741. http://dx.doi.org/10.3390/app12031741.

Full text

Abstract:

Existing end-to-end cloud registration methods are often inefficient and susceptible to noise. We propose an end-to-end point cloud registration network model, Point Transformer for Registration Network (PTRNet), that considers local and global features to improve this behavior. Our model uses point clouds as inputs and applies a Transformer method to extract their global features. Using a K-Nearest Neighbor (K-NN) topology, our method then encodes the local features of a point cloud and integrates them with the global features to obtain the point cloud’s strong global features. Comparative ex

APA, Harvard, Vancouver, ISO, and other styles

13

Kehoe, Devin H., Selvi Aybulut, and Mazyar Fallah. "Higher order, multifeatural object encoding by the oculomotor system." Journal of Neurophysiology 120, no. 6 (2018): 3042–62. http://dx.doi.org/10.1152/jn.00834.2017.

Full text

Abstract:

Previous behavioral and physiological research has demonstrated that as the behavioral relevance of potential saccade goals increases, they elicit more competition during target selection processing as evidenced by increased saccade curvature and neural activity. However, these effects have only been demonstrated for lower order feature singletons, and it remains unclear whether more complicated featural differences between higher order objects also elicit vector modulation. Therefore, we measured human saccades curvature elicited by distractors bilaterally flanking a target during a visual se

APA, Harvard, Vancouver, ISO, and other styles

14

DIACONESCU, RĂZVAN, and ALEXANDRE MADEIRA. "Encoding hybridized institutions into first-order logic." Mathematical Structures in Computer Science 26, no. 5 (2014): 745–88. http://dx.doi.org/10.1017/s0960129514000383.

Full text

Abstract:

A ‘hybridization’ of a logic, referred to as the base logic, consists of developing the characteristic features of hybrid logic on top of the respective base logic, both at the level of syntax (i.e. modalities, nominals, etc.) and of the semantics (i.e. possible worlds). By ‘hybridized institutions’ we mean the result of this process when logics are treated abstractly as institutions (in the sense of the institution theory of Goguen and Burstall). This work develops encodings of hybridized institutions into (many-sorted) first-order logic (abbreviated $\mathcal{FOL}$) as a ‘hybridization’ proc

APA, Harvard, Vancouver, ISO, and other styles

15

Galeano Weber, Elena M., Haley Keglovits, Arin Fisher, and Silvia A. Bunge. "Insights into visual working memory precision at the feature- and object-level from a hemispheric encoding manipulation." Quarterly Journal of Experimental Psychology 73, no. 11 (2020): 1949–68. http://dx.doi.org/10.1177/1747021820934990.

Full text

Abstract:

Mnemonic precision is an important aspect of visual working memory (WM). Here, we probed mechanisms that affect precision for spatial (size) and non-spatial (colour) features of an object, and whether these features are encoded and/or stored separately in WM. We probed precision at the feature-level—that is, whether different features of a single object are represented separately or together in WM—and the object-level—that is, whether different features across a set of sequentially presented objects are represented in the same or different WM stores. By manipulating whether stimuli were encode

APA, Harvard, Vancouver, ISO, and other styles

16

Kim, Minseong, and Hyun-Chul Choi. "Uncorrelated feature encoding for faster image style transfer." Neural Networks 140 (August 2021): 148–57. http://dx.doi.org/10.1016/j.neunet.2021.03.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Hassan, Ehtesham, and Lekshmi V. L. "Attention Guided Feature Encoding for Scene Text Recognition." Journal of Imaging 8, no. 10 (2022): 276. http://dx.doi.org/10.3390/jimaging8100276.

Full text

Abstract:

The real-life scene images exhibit a range of variations in text appearances, including complex shapes, variations in sizes, and fancy font properties. Consequently, text recognition from scene images remains a challenging problem in computer vision research. We present a scene text recognition methodology by designing a novel feature-enhanced convolutional recurrent neural network architecture. Our work addresses scene text recognition as well as sequence-to-sequence modeling, where a novel deep encoder–decoder network is proposed. The encoder in the proposed network is designed around a hier

APA, Harvard, Vancouver, ISO, and other styles

18

Li, Lin, Ying Ding, Bo Li, Mengqing Qiao, and Biao Ye. "Malware classification based on double byte feature encoding." Alexandria Engineering Journal 61, no. 1 (2022): 91–99. http://dx.doi.org/10.1016/j.aej.2021.04.076.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Mesgarani, N., C. Cheung, K. Johnson, and E. F. Chang. "Phonetic Feature Encoding in Human Superior Temporal Gyrus." Science 343, no. 6174 (2014): 1006–10. http://dx.doi.org/10.1126/science.1245994.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Li, N., and Y. F. Li. "Feature encoding for unsupervised segmentation of color images." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 33, no. 3 (2003): 438–47. http://dx.doi.org/10.1109/tsmcb.2003.811120.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Kondo, Aki, and Jun Saiki. "Feature-Specific Encoding Flexibility in Visual Working Memory." PLoS ONE 7, no. 12 (2012): e50962. http://dx.doi.org/10.1371/journal.pone.0050962.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

McCabe, Andrew, Terry Caelli, Geoff West, and Adam Reeves. "Theory of spatiochromatic image encoding and feature extraction." Journal of the Optical Society of America A 17, no. 10 (2000): 1744. http://dx.doi.org/10.1364/josaa.17.001744.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Ebitz, R. Becket, Jiaxin Cindy Tu, and Benjamin Y. Hayden. "Rules warp feature encoding in decision-making circuits." PLOS Biology 18, no. 11 (2020): e3000951. http://dx.doi.org/10.1371/journal.pbio.3000951.

Full text

Abstract:

We have the capacity to follow arbitrary stimulus–response rules, meaning simple policies that guide our behavior. Rule identity is broadly encoded across decision-making circuits, but there are less data on how rules shape the computations that lead to choices. One idea is that rules could simplify these computations. When we follow a rule, there is no need to encode or compute information that is irrelevant to the current rule, which could reduce the metabolic or energetic demands of decision-making. However, it is not clear if the brain can actually take advantage of this computational simp

APA, Harvard, Vancouver, ISO, and other styles

24

Cho, Jacee, and Roumyana Slabakova. "Interpreting definiteness in a second language without articles: The case of L2 Russian." Second Language Research 30, no. 2 (2014): 159–90. http://dx.doi.org/10.1177/0267658313509647.

Full text

Abstract:

This article investigates the second language (L2) acquisition of two expressions of the semantic feature [definite] in Russian, a language without articles, by English and Korean native speakers. Within the Feature Reassembly approach (Lardiere, 2009), Slabakova (2009) has argued that reassembling features that are represented overtly in the first language (L1) and mapping them onto those that are encoded indirectly, or covertly, in the L2 will present a greater difficulty than reassembling features in the opposite learning direction. An idealized scale of predictions of difficulty is propose

APA, Harvard, Vancouver, ISO, and other styles

25

Ma, Chong, Hongyang Yin, Liguo Weng, Min Xia, and Haifeng Lin. "DAFNet: A Novel Change-Detection Model for High-Resolution Remote-Sensing Imagery Based on Feature Difference and Attention Mechanism." Remote Sensing 15, no. 15 (2023): 3896. http://dx.doi.org/10.3390/rs15153896.

Full text

Abstract:

Change detection is an important component in the field of remote sensing. At present, deep-learning-based change-detection methods have acquired many breakthrough results. However, current algorithms still present issues such as target misdetection, false alarms, and blurry edges. To alleviate these problems, this work proposes a network based on feature differences and attention mechanisms. This network includes a Siamese architecture-encoding network that encodes images at different times, a Difference Feature-Extraction Module (DFEM) for extracting difference features from bitemporal image

APA, Harvard, Vancouver, ISO, and other styles

26

St-Yves, Ghislain, and Thomas Naselaris. "The feature-weighted receptive field: an interpretable encoding model for complex feature spaces." NeuroImage 180 (October 2018): 188–202. http://dx.doi.org/10.1016/j.neuroimage.2017.06.035.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Aizezi, Yasen, Anwar Jamal, Ruxianguli Abudurexiti, and Mutalipu Muming. "Research on Digital Forensics Based on Uyghur Web Text Classification." International Journal of Digital Crime and Forensics 9, no. 4 (2017): 30–39. http://dx.doi.org/10.4018/ijdcf.2017100103.

Full text

Abstract:

This paper mainly discusses the use of mutual information (MI) and Support Vector Machines (SVMs) for Uyghur Web text classification and digital forensics process of web text categorization: automatic classification and identification, conversion and pretreatment of plain text based on encoding features of various existing Uyghur Web documents etc., introduces the pre-paratory work for Uyghur Web text encoding. Focusing on the non-Uyghur characters and stop words in the web texts filtering, we put forward a Multi-feature Space Normalized Mutual Information (M-FNMI) algorithm and replace MI bet

APA, Harvard, Vancouver, ISO, and other styles

28

Gheisari, Soheila, Daniel Catchpoole, Amanda Charlton, Zsombor Melegh, Elise Gradhand, and Paul Kennedy. "Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding." Diagnostics 8, no. 3 (2018): 56. http://dx.doi.org/10.3390/diagnostics8030056.

Full text

Abstract:

Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathology study is considered the gold standard for classification of neuroblastoma histological images, computers can help to extract many more features some of which may not be recognizable by human eyes. This paper, proposes a combination of Scale Invariant Feature Transform with feature encoding algorithm to extract highly discriminative features. Then, distinctive image features are classif

APA, Harvard, Vancouver, ISO, and other styles

29

Lv, Yalong, Shengwei Tian, Long Yu, and Ruonan Zhang. "Water Body Semantic Information Description and Recognition Based on Multimodal Models." International Journal of Computational Intelligence and Applications 18, no. 02 (2019): 1950015. http://dx.doi.org/10.1142/s1469026819500159.

Full text

Abstract:

To solve the problems from using single-layer features in traditional water body identification models, such as the lack of local descriptors, large quantization errors, and the lack of semantic information descriptions, a multimodal model is proposed based on the different levels of feature knowledge. First, based on the multidescriptor hierarchical feature, the middle-level local feature extraction of the water body is achieved, and, combined with the convolutional neural network, the high-order global features of the water body are extracted. Then, the image features are hierarchically norm

APA, Harvard, Vancouver, ISO, and other styles

30

Xu, Mengxi, Yingshu Lu, and Xiaobin Wu. "Annular Spatial Pyramid Mapping and Feature Fusion-Based Image Coding Representation and Classification." Wireless Communications and Mobile Computing 2020 (September 11, 2020): 1–9. http://dx.doi.org/10.1155/2020/8838454.

Full text

Abstract:

Conventional image classification models commonly adopt a single feature vector to represent informative contents. However, a single image feature system can hardly extract the entirety of the information contained in images, and traditional encoding methods have a large loss of feature information. Aiming to solve this problem, this paper proposes a feature fusion-based image classification model. This model combines the principal component analysis (PCA) algorithm, processed scale invariant feature transform (P-SIFT) and color naming (CN) features to generate mutually independent image repre

APA, Harvard, Vancouver, ISO, and other styles

31

Maniglia, Mariana R., and Alessandra S. Souza. "Age Differences in the Efficiency of Filtering and Ignoring Distraction in Visual Working Memory." Brain Sciences 10, no. 8 (2020): 556. http://dx.doi.org/10.3390/brainsci10080556.

Full text

Abstract:

Healthy aging is associated with decline in the ability to maintain visual information in working memory (WM). We examined whether this decline can be explained by decreases in the ability to filter distraction during encoding or to ignore distraction during memory maintenance. Distraction consisted of irrelevant objects (Exp. 1) or irrelevant features of an object (Exp. 2). In Experiment 1, participants completed a spatial WM task requiring remembering locations on a grid. During encoding or during maintenance, irrelevant distractor positions were presented. In Experiment 2, participants enco

APA, Harvard, Vancouver, ISO, and other styles

32

Fu, Qiang, and Hongbin Dong. "Breast Cancer Recognition Using Saliency-Based Spiking Neural Network." Wireless Communications and Mobile Computing 2022 (March 24, 2022): 1–17. http://dx.doi.org/10.1155/2022/8369368.

Full text

Abstract:

The spiking neural networks (SNNs) use event-driven signals to encode physical information for neural computation. SNN takes the spiking neuron as the basic unit. It modulates the process of nerve cells from receiving stimuli to firing spikes. Therefore, SNN is more biologically plausible. Although the SNN has more characteristics of biological neurons, SNN is rarely used for medical image recognition due to its poor performance. In this paper, a reservoir spiking neural network is used for breast cancer image recognition. Due to the difficulties of extracting the lesion features in medical im

APA, Harvard, Vancouver, ISO, and other styles

33

Linhardt, Timothy, and Ananya Sen Gupta. "Sonar feature representation with autoencoders and generative adversarial networks." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A178. http://dx.doi.org/10.1121/10.0018583.

Full text

Abstract:

Feature representation in the littoral sonar space is a complicated field due to prevalent channel noise from sound reflections as well as diffraction. The response of a sound wave as it interacts with an object provides insight into the nature of the object itself such as geometry and material composition. We approach automated target recognition in this space with feature representation using autoencoders and generative adversarial networks. Through empirical analysis of learned encoding spaces, the dimensionalities of principal features in our sonar data sets are estimated. Real and complex

APA, Harvard, Vancouver, ISO, and other styles

34

Liu, Yangyang, Minghua Tian, Chang Xu, and Lixiang Zhao. "Neural network feature learning based on image self-encoding." International Journal of Advanced Robotic Systems 17, no. 2 (2020): 172988142092165. http://dx.doi.org/10.1177/1729881420921653.

Full text

Abstract:

With the rapid development of information technology and the arrival of the era of big data, people’s access to information is increasingly relying on information such as images. Today, image data are showing an increasing trend in the form of an index. How to use deep learning models to extract valuable information from massive data is very important. In the face of such a situation, people cannot accurately and timely find out the information they need. Therefore, the research on image retrieval technology is very important. Image retrieval is an important technology in the field of computer

APA, Harvard, Vancouver, ISO, and other styles

35

Gong, Dihong, Zhifeng Li, Weilin Huang, Xuelong Li, and Dacheng Tao. "Heterogeneous Face Recognition: A Common Encoding Feature Discriminant Approach." IEEE Transactions on Image Processing 26, no. 5 (2017): 2079–89. http://dx.doi.org/10.1109/tip.2017.2651380.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Fiser, József, and Richard N. Aslin. "Encoding Multielement Scenes: Statistical Learning of Visual Feature Hierarchies." Journal of Experimental Psychology: General 134, no. 4 (2005): 521–37. http://dx.doi.org/10.1037/0096-3445.134.4.521.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Mammarella, Nicola, and Beth Fairfield. "Source monitoring: The importance of feature binding at encoding." European Journal of Cognitive Psychology 20, no. 1 (2008): 91–122. http://dx.doi.org/10.1080/09541440601112522.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Cowan, S. C., Peter J. Blamey, Joseph I. Alcantara, Lesley A. Whitford, Graeme Clark, and Geoff Plant. "Speech feature encoding through an electrotactile speech processor Robert." Journal of the Acoustical Society of America 86, S1 (1989): S83. http://dx.doi.org/10.1121/1.2027685.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Krahe, Rüdiger, Gabriel Kreiman, Fabrizio Gabbiani, Christof Koch, and Walter Metzner. "Stimulus Encoding and Feature Extraction by Multiple Sensory Neurons." Journal of Neuroscience 22, no. 6 (2002): 2374–82. http://dx.doi.org/10.1523/jneurosci.22-06-02374.2002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Zhao, Yanna, Xu Zhao, Ruotian Luo, and Yuncai Liu. "Person Re-identification by encoding free energy feature maps." Multimedia Tools and Applications 75, no. 8 (2015): 4795–813. http://dx.doi.org/10.1007/s11042-015-2503-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Altınçay, Hakan, and Zafer Erenel. "Ternary encoding based feature extraction for binary text classification." Applied Intelligence 41, no. 1 (2014): 310–26. http://dx.doi.org/10.1007/s10489-014-0515-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Wang, Tong, Wenan Tan, and Jianxin Xue. "A Data Mining Method For Improving the Prediction Of Bioinformatics Data." Journal of Physics: Conference Series 2137, no. 1 (2021): 012067. http://dx.doi.org/10.1088/1742-6596/2137/1/012067.

Full text

Abstract:

Abstract The composition of proteins nearly correlated with its function. Therefore, it is very ungently important to discuss a method that can automatically forecast protein structure. The fusion encoding method of PseAA and DC was adopted to describe the protein features. Using this encoding method to express protein sequences will produce higher dimensional feature vectors. This paper uses the algorithm of predigesting the characteristic dimension of proteins. By extracting significant feature vectors from the primitive feature vectors, eigenvectors with high dimensions are changed to eigen

APA, Harvard, Vancouver, ISO, and other styles

43

Yang, Jia-Quan, Chun-Hua Chen, Jian-Yu Li, Dong Liu, Tao Li, and Zhi-Hui Zhan. "Compressed-Encoding Particle Swarm Optimization with Fuzzy Learning for Large-Scale Feature Selection." Symmetry 14, no. 6 (2022): 1142. http://dx.doi.org/10.3390/sym14061142.

Full text

Abstract:

Particle swarm optimization (PSO) is a promising method for feature selection. When using PSO to solve the feature selection problem, the probability of each feature being selected and not being selected is the same in the beginning and is optimized during the evolutionary process. That is, the feature selection probability is optimized from symmetry (i.e., 50% vs. 50%) to asymmetry (i.e., some are selected with a higher probability, and some with a lower probability) to help particles obtain the optimal feature subset. However, when dealing with large-scale features, PSO still faces the chall

APA, Harvard, Vancouver, ISO, and other styles

44

Son, Chang-Hwan. "Leaf Spot Attention Networks Based on Spot Feature Encoding for Leaf Disease Identification and Detection." Applied Sciences 11, no. 17 (2021): 7960. http://dx.doi.org/10.3390/app11177960.

Full text

Abstract:

This study proposes a new attention-enhanced YOLO model that incorporates a leaf spot attention mechanism based on regions-of-interest (ROI) feature extraction into the YOLO framework for leaf disease detection. Inspired by a previous study, which revealed that leaf spot attention based on the ROI-aware feature extraction can improve leaf disease recognition accuracy significantly and outperform state-of-the-art deep learning models, this study extends the leaf spot attention model to leaf disease detection. The primary idea is that spot areas indicating leaf diseases appear only in leaves, wh

APA, Harvard, Vancouver, ISO, and other styles

45

Baviskar, Amol G., and S. S. Pawale. "Efficient Domain Search for Fractal Image Compression Using Feature Extraction Technique." Advanced Materials Research 488-489 (March 2012): 1587–91. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.1587.

Full text

Abstract:

Fractal image compression is a lossy compression technique developed in the early 1990s. It makes use of the local self-similarity property existing in an image and finds a contractive mapping affine transformation (fractal transform) T, such that the fixed point of T is close to the given image in a suitable metric. It has generated much interest due to its promise of high compression ratios with good decompression quality. Image encoding based on fractal block-coding method relies on assumption that image redundancy can be efficiently exploited through block-self transformability. It has sho

APA, Harvard, Vancouver, ISO, and other styles

46

SCHWARTZ, WILLIAM ROBSON, and HELIO PEDRINI. "IMPROVED FRACTAL IMAGE COMPRESSION BASED ON ROBUST FEATURE DESCRIPTORS." International Journal of Image and Graphics 11, no. 04 (2011): 571–87. http://dx.doi.org/10.1142/s0219467811004251.

Full text

Abstract:

Fractal image compression is one of the most promising techniques for image compression due to advantages such as resolution independence and fast decompression. It exploits the fact that natural scenes present self-similarity to remove redundancy and obtain high compression rates with smaller quality degradation compared to traditional compression methods. The main drawback of fractal compression is its computationally intensive encoding process, due to the need for searching regions with high similarity in the image. Several approaches have been developed to reduce the computational cost to

APA, Harvard, Vancouver, ISO, and other styles

47

Fan, Xiaojin, Mengmeng Liao, Lei Chen, and Jingjing Hu. "Few-Shot Learning for Multi-POSE Face Recognition via Hypergraph De-Deflection and Multi-Task Collaborative Optimization." Electronics 12, no. 10 (2023): 2248. http://dx.doi.org/10.3390/electronics12102248.

Full text

Abstract:

Few-shot, multi-pose face recognition has always been an interesting yet difficult subject in the field of pattern recognition. Researchers have come up with a variety of workarounds; however, these methods make it either difficult to extract effective features that are robust to poses or difficult to obtain globally optimal solutions. In this paper, we propose a few-shot, multi-pose face recognition method based on hypergraph de-deflection and multi-task collaborative optimization (HDMCO). In HDMCO, the hypergraph is embedded in a non-negative image decomposition to obtain images without pose

APA, Harvard, Vancouver, ISO, and other styles

48

Chen, Dong, Guiqiu Xiang, Jiju Peethambaran, Liqiang Zhang, Jing Li, and Fan Hu. "AFGL-Net: Attentive Fusion of Global and Local Deep Features for Building Façades Parsing." Remote Sensing 13, no. 24 (2021): 5039. http://dx.doi.org/10.3390/rs13245039.

Full text

Abstract:

In this paper, we propose a deep learning framework, namely AFGL-Net to achieve building façade parsing, i.e., obtaining the semantics of small components of building façade, such as windows and doors. To this end, we present an autoencoder embedding position and direction encoding for local feature encoding. The autoencoder enhances the local feature aggregation and augments the representation of skeleton features of windows and doors. We also integrate the Transformer into AFGL-Net to infer the geometric shapes and structural arrangements of façade components and capture the global contextua

APA, Harvard, Vancouver, ISO, and other styles

49

Li, Xiaoqiang, Dan Wang, and Yin Zhang. "Representation for Action Recognition Using Trajectory-Based Low-Level Local Feature and Mid-Level Motion Feature." Applied Computational Intelligence and Soft Computing 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/4019213.

Full text

Abstract:

The dense trajectories and low-level local features are widely used in action recognition recently. However, most of these methods ignore the motion part of action which is the key factor to distinguish the different human action. This paper proposes a new two-layer model of representation for action recognition by describing the video with low-level features and mid-level motion part model. Firstly, we encode the compensated flow (w-flow) trajectory-based local features with Fisher Vector (FV) to retain the low-level characteristic of motion. Then, the motion parts are extracted by clustering

APA, Harvard, Vancouver, ISO, and other styles

50

Butt, Ammar Mohsin, Muhammad Haroon Yousaf, Fiza Murtaza, Saima Nazir, Serestina Viriri, and Sergio A. Velastin. "Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition." Applied Sciences 10, no. 12 (2020): 4412. http://dx.doi.org/10.3390/app10124412.

Full text

Abstract:

Human action recognition has gathered significant attention in recent years due to its high demand in various application domains. In this work, we propose a novel codebook generation and hybrid encoding scheme for classification of action videos. The proposed scheme develops a discriminative codebook and a hybrid feature vector by encoding the features extracted from CNNs (convolutional neural networks). We explore different CNN architectures for extracting spatio-temporal features. We employ an agglomerative clustering approach for codebook generation, which intends to combine the advantages

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!