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Software Engineer or Data Scientist: thoughts

Flower classification is one of the most widely studied example of machine learning. On the other hand it is also very challenging that there is diversity of flower species and it is very hard to classify them when they can be very similar to each other. Therefore, this subject has already become crucial.

Classification of iris flowers is the standard dataset. One of the chanllenge In flower classification is lack of labeled data which can result in performance loss. Recognizing a large number of classes within one category – that of flowers is also an challenge. If we have more classes then combinations of features can improve classification performance. In flower classification we mainly used the feature like local shape/texture, the shape of the boundary, the overall spatial distribution of petals, and the colour Classifying flowers which poses an extra challenge over the categories such as bikes, cars and cats, because of the large similarity between classes.

Another challenge with flower classification is that flowers are non-rigid objects that can deform in many ways, and consequently there is also a large variation within classes. More the no of classes add more complexity.

What distinguishes one flower from another can sometimes be just the colour, sometimes the shape and sometimes patterns on the petals. The difficulty lies in finding suitable features to represent colour, shape, patterns etc.

References and further reading



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