3 A Survey of Shape Feature Extraction Techniques

"A picture is worth one thousand words". This proverb comes from Confucius a Chinese philosopher about 2500 years ago. Now, the essence of these words is universally understood. A picture can be magical in its ability to quickly communicate a complex story or a set of ideas that can be recalled by the viewer later in time. Visual information plays an important role in our society, it will play an increasingly pervasive role in our lives, and there will be a growing need to have these sources processed further. The pictures or images are used in many application areas like architectural and engineering design, fashion, journalism, advertising, entertainment, etc. Thus it provides the necessary opportunity for us to use the abundance of images. However, the knowledge will be useless if one can't find it. Face to the substantive and increasing apace images, how to search and to retrieve the images that we are interested in facility is a fatal problem: it brings a necessity for image retrieval systems. As we know, visual features of the images provide a description of their content. Content-based image retrieval (CBIR), emerged as a promising mean for retrieving images and browsing large images databases. CBIR has been a topic of intensive research in recent years. It is the process of retrieving images from a collection based on automatically extracted features from those images. This paper focuses on presenting a survey of the existing approaches of shape-based feature extraction. Efficient shape features must present some essential properties such as: • identifiability: shapes which are found perceptually similar by human have the same features that are different from the others. • translation, rotation and scale invariance: the location, the rotation and the scaling changing of the shape must not affect the extracted features. • affine invariance: the affine transform performs a linear mapping from coordinates system to other coordinates system that preserves the "straightness" and "parallelism" of lines. Affine transform can be constructed using sequences of translations, scales, flips, rotations and shears. The extracted features must be as invariant as possible with affine transforms. • noise resistance: features must be as robust as possible against noise, i.e., they must be the same whichever be the strength of the noise in a give range that affects the pattern. O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m