Recognition and identification of target images using feature based retrieval in UAV missions

Singh, Shweta; Rao, Dattaraj

doi:10.1109/ncvpripg.2013.6776165

Cited by 3 publications

(1 citation statement)

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“…In recent years, the need for efficient content-based image retrieval has increased tremendously in many application areas, such as biomedicine [1,2], military [3], commerce, education, and web image classification and retrieval [4][5][6]. Currently, rapid and effective image retrieval from a large-scale image database is becoming an important and challenging research topic [7].…”

Section: Introductionmentioning

confidence: 99%

An Advanced Approach to Extraction of Colour Texture Features Based on GLCM

Benčo

Hudec

Kamencay

et al. 2014

International Journal of Advanced Robotic Systems

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This paper discusses research in the area of texture image classification. More specifically, the combination of texture and colour features is researched. The principle objective is to create a robust descriptor for the extraction of colour texture features. The principles of two well-known methods for grey-level texture feature extraction, namely GLCM (grey-level co-occurrence matrix) and Gabor filters, are used in experiments. For the texture classification, the support vector machine is used. In the first approach, the methods are applied in separate channels in the colour image. The experimental results show the huge growth of precision for colour texture retrieval by GLCM. Therefore, the GLCM is modified for extracting probability matrices directly from the colour image. The method for 13 directions neighbourhood system is proposed and formulas for probability matrices computation are presented. The proposed method is called CLCM (colour-level co-occurrence matrices) and experimental results show that it is a powerful method for colour texture classification.

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