A Better Approach to Attain Appropriate Images from a Collection of Images Using Fuzzy-C-Means

A common mistake in the concept of image search is that the technology is based on detecting and processing the information in the image itself. Searching for an images works like this; the meta data of the image is indexed and stored in a large database or repository and when a search query(keywords) is entered the image search engine accesses the index, and queries are matched with the stored information. The results are presented in no particular order of relevancy. The effectiveness of an image search engine depends on the relevance of the results it returns, and the clustering algorithm plays a big role. This paper compares the working of two clustering algorithms K-Means algorithm and Fuzzy C Means algorithm. When using Fuzzy C Means algorithm, one image can appear in more than one cluster unlike K-Means which is hard based grouping. Through the results it can be clearly seen that using Fuzzy C Means brings about a level of flexibility and proper clustering. The user can access images from an image search engine, picture library, trained data sets, etc. Hence there is a necessity of providing the user more accurate collection of images which can be done through Fuzzy C Means clustering.