IMPLEMENTATION OF IMAGE BASED FLOWER CLASSIFICATION SYSTEM

Abstract

In today’s world, automatic recognition of flowers using computer technology is of great social benefits. Classification of flowers has various applications such as floriculture, flower searching for patent analysis and much more. Floriculture industry consists of flower trade, nursery and potted plants, seed and bulb production, micro propagation and extraction of essential oil from flowers. For all the above, automation of flower classification is very essential step. However, classifying flowers is not an easy task due to difficulties such as deformations of petals, inter and intra class variability, illumination and many more. The flower classification system proposed in this paper uses a novel concept of developing visual vocabulary for simplifying the complex task of classifying flower images. Separate vocabularies for color, shape and texture features are created and then they are combined into final classifier. In this process firstly, an image is segmented using grabcut method. Secondly, features are extracted using appropriate algorithms such as SIFT descriptors for shape, HSV model for color and MR8filter bank for texture extraction. Finally, the classification is done with multiboost classifier. Results are represented on 17 categories of flower species and seem to have efficient performance.