In this case, the classification decision is based on the histogram output. If we then remember tough that the histogram is just the measure of quantity of one color over another, we understand that the histogram output would be the same for, say, a mix of diced or whole vegetables – as both have the same color proportion over the whole image.
To overcome this while preserving invariance to scale, rotational and spatial differences, a new method, called ‘color spots distance’ or just ‘color distance’ was invented and explored. The method is based on the color histogram with the extension that calculates a measure of proximity of the spots of a certain color over the image. This measure accounts not only for the color itself, but also for the degree of color distribution, thus giving different results for, say, a whole tomato (where all red components are compact) against a sliced one, where they are distributed.
The new method has been proved against the sample set of 15 fruits of different kind, showing the recognition quality of about 90% ‘in vitro’. Field experiments following the lab tests are to follow, yet the preliminary results are quite encouraging. To compare – the texture method referred above shows only as little as 40% under the same conditions.
The test results chart and misclassification graph for the color distance method are given in the next post.