Color is the Most Demonstrative Visual Feature and Studied in the Context of CBIR

In 2010, S.Arivazhagan, R.Newlin Shebiah, S.Selva Nidhyanandhan, L.Ganesan had done their research, and they published their research named ‘Fruit Recognition using Color and Texture Features’. The objective of this project is to recognize a fruit based on four common features including intensity, color, shape, and texture of the object. Moreover,

These 16 features included 12 features calculated based on the 6 multispectral bands, which is mean value and standard deviation of these bands. In addition, we chose intensity, texture-variance, texture-mean, and NDVI (Normalized Difference Vegetation Index) for classifications. Finally, training samples were selected for each classification category based on the previously segmented and merged objects

To understand what color is, we first need to understand what light is. Light, as perceived by humans, is simply electromagnetic radiation with wavelengths between roughly 380 nm and 740 nm. Wavelengths below 380 nm and above 740 nm cannot be seem by the human eye. Electromagnetic radiation with a wavelength just below 380 nm is known as ultraviolet radiation. Electromagnetic radiation with a wavelength just above 740 nm is known as infrared radiation. The sun, black lights and fluorescent lamps are all sources of ultraviolet light. Heat is a source of infrared radiation, which is how thermal vision works.

With the use of PCA, it became difficult to determine the spectral signature of objects for a long time. In the new data cube a “pixel profile” is not a spectral signature.

In accession to the binary images, the proposed method may be tested on discrete color images also. These type of

The goal of the feature extraction and selection is to reduce the dimension of the data. In this experiment the dimension of the AVIRIS and HYDICE images reduced to 20 from 220 and 191 respectively using PCA. From the PCA analysis we can see that image of principal component 1 is brightest and sharpest than other PCA image which is illustrated in figure-2.

Colour schemes are used to unify artworks and it consists of three common schemes: warm, cool, and neutral. The warm colour scheme is vivid and energetic and tends to advance in space. It consists of red, orange and yellow. Cool colours give an impression of calm, and create a soothing impression. Blue, green and violet are a part of this colour scheme. Neutral colours give a feeling of calm and quiet atmospheres and it consist of grays, blacks and whites. The monochromatic colour scheme is based on several values of one hue. The analogous colour scheme is made up of hues that are next to each other on the colour wheel. Complementary colours are

Colour is most extensively used feature for image retrieval. Several techniques such as colour coherence vector, the colour co-occurrence matrix, vector quantization, and colour moments are used to extract colour feature from original images. Normally colours are defined in three dimensional colour spaces, which are RGB (Red, Green, and Blue), HSV (Hue, Saturation, and Value) or HSB (Hue, Saturation, and Brightness)

Texture is one of the crucial primitives in human vision and texture features have been used to identify contents of images. Examples are identifying crop fields and mountains from aerial image domain. Moreover, texture can be used to describe contents of images, such as clouds, bricks, hair, etc. Both identifying and describing characteristics of texture are accelerated when texture is integrated with color, hence the details of the important features of image objects for human vision can be provided. One crucial distinction between color and texture features is that color is a point, or pixel, property, whereas texture is a local-neighborhood property. The main motivation for using texture is the identifying and describing

For example, since red is the longest wavelength it is perceived by most people as a powerful color, which to them represents energy, strength and excitement. It grabs peoples’ attention, and hence it effectiveness in traffic lights. For many people red is perceived as a demanding and aggressive color. On the other hand, the color blue causes the opposite reactions as the color red. For most people, blue is perceived as peaceful and tranquil, and physiologically this color causes the body to produce calming chemicals, which is why it is often used as a color of paint in bedrooms.

A method to identify DR using digital signal processing and image processing techniques has been proposed by Iqbal et.al. [iii]. The methods used by them includes colour space conversion, zero padding of image edges, median filtering and histogram equalization with overlap mean for the image pre-processing stage.

It is based on Local Binary Patterns (LBP) features [3], which have shown to be a powerful and computationally efficient feature extractor. Then Chi-square based classification method is used for Authentication.

False-coloured images in videos S1 and S4 were highlighted by adding to the original images a red overlay image that was an estimate of the rate of change in the image. To compute the overlay image, we

Color is defined as “the quality of an object or substance with respect to light reflected by the object, usually determined visually by measurement of hue, saturation, and brightness of the reflected light; saturation or chroma; hue” (Webster’s Dictionary). Color is an extremely powerful psychological tool used across the globe. Our world would be bland and boring without the use of color in our everyday lives. Using color psychology can encourage sales, calm a crowd, and even help a person send a positive (or negative) message.

Colour can be identified by two different methods which are objectively and subjectively. The terms objectively in colour ontology is a method that using the facts by referring to the laws of physics, chemistry and physiology. Subjectively is the term that referred to the psychological concept.