A Perceptual Visual Feature Extraction Method Achieved by Imitating V1 and V4 of the Human Visual System

In this paper we present a Visual feature extraction using improvised SVM and KNN classifiers. The proposed method is an automatic, stable, quick response automatic segmentation, followed by feature extraction and classification to detect spam from the images and the text. The KNN classifier is used to extract features by predicting nearest neighbour while SVM, analyze the data for classification and regression. The hybrid-based Visual feature extraction and classification is elaborated wherein this work discuss the proposed approach which incorporated using improvised SVM and KNN classifier. Moreover, identified patterns via feature extraction method by means of a minimum number of features that are effective in discriminating pattern classes. With all the aforementioned concepts elaborated, the experimental set-up was elaborated with the experimental task, and the results of the character recognition component are further elucidated.

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A Robust Visual-Feature-Extraction Method for Simultaneous Localization and Mapping in Public Outdoor Environment

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2015.p0011 ◽

2015 ◽

Vol 19 (1) ◽

pp. 11-22 ◽

Cited By ~ 4

Author(s):

Gangchen Hua ◽

◽

Osamu Hasegawa ◽

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Low Cost ◽

Simultaneous Localization And Mapping ◽

Video Camera ◽

Outdoor Environment ◽

Visual Feature ◽

Feature Extraction Method ◽

Localization And Mapping ◽

Single Lens

We describe a new feature extraction method based on the geometric structure of matched local feature points that extracts robust features from an image sequence and performs satisfactorily in highly dynamic environments. Our proposed method is more accurate than other such methods in appearance-only simultaneous localization and mapping (SLAM). Compared to position-invariant robust features [1], it is also more suitable for low-cost, single lens cameras with narrow fields of view. Testing our method in an outdoor environment at Shibuya Station. We captured images using a conventional hand-held single-lens video camera. These environments of experiments are public environments without any planned landmarks. Results have shown that the proposed method accurately obtains matches for two visual-feature sets and that stable, accurate SLAM is achieved in dynamic public environments.

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Visual Feature Extraction under Wavelet Domain for Image Retrieval

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.277-279.206 ◽

2005 ◽

Vol 277-279 ◽

pp. 206-211 ◽

Cited By ~ 1

Author(s):

Won Bae Park ◽

Eun Ju Ryu ◽

Young Jun Song ◽

Jae Hyeong Ahn

Keyword(s):

Feature Extraction ◽

Image Retrieval ◽

Frequency Band ◽

Spatial Information ◽

Image Database ◽

Wavelet Domain ◽

Visual Feature ◽

Feature Extraction Method ◽

Feature Vectors ◽

Visual Feature Extraction

In this paper, we propose a new visual feature extraction method for content-based image retrieval (CBIR) based on wavelet transform which has both spatial-frequency and multi-resolution characteristics. We extract visual features for each frequency band in wavelet transformation and use them for CBIR. The lowest frequency band involves utilizing the spatial information of an original image. We extract 64 feature vectors using fuzzy homogeneity in the wavelet domain, which considers both the wavelet coefficients and the spatial information of each coefficient. In addition, we extract 3 feature vectors using the energy values of high frequency bands, and store those to the image database. As a query, we retrieve the most similar image from the image database according to the 10 largest homograms (normalized fuzzy homogeneity vectors) and 3 energy values. Simulation results show that the proposed method has good accuracy in image retrieval using 90 texture images.

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