You can use a threshold method

As stated by Navdeep Singh, there are different types of Pixel Classification depending on one's interest in the type of Image Classification like Referecs2-5 etc.

But a New Approach of Object Based Image Analysis as Alternative to Pixel BasedImage Analysis may be of Interest and is given under Reference 1 below.

1. Object Based Image Analysis -Alternative to Pixel Based Image Analysis:

By Y.Gao and J.F. Mas

http://www.isprs.org/proceedings/xxxviii/4-c1/sessions/Session1/6589_Y_Gao_Proc_pap.pdf

2. Contextual image classification, a topic of pattern recognition in computer vision, is an approach of classification based on contextual information in images. "Contextual" means this approach is focusing on the relationship of the nearby pixels, which is also called neighbourhood. The goal of this approach is to classify the images by using the contextual information

http://en.wikipedia.org/wiki/Contextual_image_classification

3. Image Analysis - Classification by by HI Works - ‎Related articles

Classification includes a broad range of decision-theoretic approaches to the identification of images (or parts thereof). All classification algorithms are based on the assumption that the image in question depicts one or more features (e.g., geometric parts in the case of a manufacturing classification system, or spectral regions in the case of remote sensing, as shown in the examples below) and that each of these features belongs to one of several distinct and exclusive classes. The classes may be specified a priori by an analyst (as in supervised classification) or automatically clustered (i.e. as in unsupervised classification) into sets of prototype classes, where the analyst merely specifies the number of desired categories. (Classification and segmentation have closely related objectives, as the former is another form of component labeling that can result in segmentation of various features in a scene.)

http://homepages.inf.ed.ac.uk/rbf/HIPR2/classify.htm

4. Non-parametric Classification of Pixels Under Varying Outdoor Illumination. Shashi D. Buluswar and Bruce A. Draper.

Non-parametric Classification of Pixels Under Varying Outdoor Illumination. Shashi D. Buluswar and Bruce A. Draper.

http://www.cs.colostate.edu/~draper/papers/buluswar_iuw94.pdf

5. Robust Pixel Classification for 3D Modeling with Structured Light

by Yi Xu and Daniel G. Aliaga.

https://www.cs.purdue.edu/cgvlab/papers/aliaga/gi07.pdf

P.S.

Thank You !