Essay On Image Classification

Since classifiers cover a wide variety of uses there are several categories that a classifier can be used for, as a Descriptive classifier (DCL) which is used for describing an object or a person. The story “TIMBER” the signer describes a lumberjack’s appearance. The signer describes the lumberjacks’ large muscles and large chest; he describes the plaid shirt the lumberjack is wearing as well. Locative Classifiers (LCL) are representing an object in a specific place and sometimes movement. The handshape is given followed by spatial or locative information. In the story “TIMBER” the signer uses several Locative classifiers, one of them is when he shows the forest being in front of the lumberjack.

Neurons are information- processing units in the central nervous system that receive and transmit information. It is made up of an axon, dendrites and a cell body. The nucleus and cytoplasm are contained in the cell body. The axon starts from the cell body, dividing into smaller branches and then ends at the nerve terminals. The dendrites also branch from the cell body, receiving information from the other neurons. Axons from other neurons forms

As well as these there are also the axon of the cell which is covered in myelin sheaths which carried information away from the cell body and hands the action potentials, these are small short bursts of change in the electrical charge of the axon membrane through openings of ion channels, off to the following neurons dendrites through terminal buttons at the end of the axons. Whenever an action potential is passed through these terminal buttons it releases a chemicals that pass on the action potential on to the next neuron through the terminal button and dendrite connection. The chemicals that are

Where xi is the input i and wik is the weight connecting input i to neuron k, set all weights to small random values, positive and negative, usually in the ranges from - 1 to +1. Apply one training sample to the input layer, that is x0 .... xN −1 and note the corresponding desired output vector, i.e.,. y0 ....yM −1.

1.1 Sort and classify objects by one attribute into two or more groups, with increasing accuracy.

Neural Networks consist of three layers which are interconnected. The first layer consists of input neurons. Those neurons send the data to the second layer, which in turn sends the output neurons to the third layer.

We have used support vector machine (SVM) for classification task. We have used RBF kernel for training the classifier. 10 fold cross-validation is used for determining cost parameter C and best kernel width for RBF kernel function. If we perform classification without any feature selection or feature extraction then the accuracy is 48.99% and 65.82% for AVIRIS and HYDICE image respectively which is very poor and it highly motivates us to apply feature reduction technique. In table II we have shown the classification accuracy for each of the pair of class for PCA, MI and PCA-QMI.

Within the nerve net of cnidarians you will find sensory neurons, motor neurons, and intermediate neurons. The intermediate neurons carry messages from the sensory neurons to the motor neurons, and some of these could possibly be organized into ganglia. In the body there are two layers of cells: nerve cells and body cells. The nerve cells help to coordinate the actions of some body cells that are within the net. For instance, if the body is touched, the whole body will react (Cnidarians).

The neurons communicate by sending electrical impulses called the 'nerve impulse' through the axon. The pre-synaptic neuron is activated therefore the neurotransmitters at end of the pre-synaptic neuron release the message into the synapse which activates the receptors on the post-synaptic neuron.

The way we think, process, and function is conducted through our nervous system. It is composed of nerve cells called neurons. The brain has 100 billion neurons. They receive an electrical impulse that excites one neuron cell and starts a chain reaction from one to the next and initiates an action. A neuron is composed of a cell body, and has dendrites and an axon. Dendrites are thin tendrils that stretch from the neuron to receive the electrical signal. It passes then on through the cell body to the axon, a long thin strand, that passes it on to the next neuron. Neurons do not touch each other. The junction between neurons is called a synapse. They pass on messages through chemicals called neurotransmitters that jump from the axon and bind to a receptor site on the next neuron’s dendrites across a gap called the synaptic cleft.

Also, there are some Martinotti(triangular or polygonal shaped multipolar neurons) and granular neurons. Afferent fibers projecting from the thalamus, corticocortical connections come to here and IV layer. These synapses both efferent cortical neurons and intercortical( granular, Martinotti and horizontal types)neurons.

The brain has a part called the cerebral cortex(gray matter) which is made up of 3 to 6 layers of neurons.[7] A neuron has three parts namely axon, dendrites and cell body.[6] Neurons are classified as principal(projection) neurons and interneurons. Principal neurons transmit information to other neurons in the brain and form excitatory synapses. Interneurons in the CNS transmit impulses locally and form inhibitory synapse.[7] Two common types of pathways for these neurons include recurrent feedback pathways and feed-forward pathways.[9](Figure 2)

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

It can then be realized that the convention of the recognition scene is employed

Neural networks are an information processing unit that is made up of neurons. According to Dr. Robert Hecht-Nielsen it is “…a computing system made up of a number simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.”(Neural Network Primer: Part 1, Maureen Caudill, Al Expert, February 1989) Therefore these neurons receive signals through dendrites and conduct impulses in a corresponding style recording a visual stimulus within ones brain cells. The activity of one nerve cell directly affects the other nerve cells. Neural networks have the ability to replace functions in targeted areas enabling the damaged areas functions to be reinstalled in other areas. Neural networks are made up of layers that consist of interconnected