1. An image classification apparatus, comprising: a computer; and programming executable on the computer for patch classifying an input image by performing steps comprising: (a) performing subscene modeling by: (i) segmenting an input image training set from an image database into one or more segmented regions based on an input pool of N subscene categories; (ii) extracting a set of training patch feature vectors for each of the segmented regions corresponding to the input pool of N subscene categories; and (iii) forming a subscene model pool comprising training patch feature vectors for each for the input pool of N subscene categories; (b) performing subscene recognition by: (i) segmenting the input image into a set of input regions; (ii) extracting patch features corresponding to each of the set of input regions to form a set of extracted patch feature vectors; and (iii) filtering the set of extracted patch features against the subscene model pool to form a filtered output; and (c) setting a subscene to one of the group consisting of the training patch feature vectors and the set of extracted patch feature vectors; (d) dividing the subscene into a set of image patches; and (e) describing each image patch by a set of concatenated image patch feature vectors {right arrow over (V)} image — patch comprising: (i) a color feature vector {right arrow over (V)} color ; and (ii) a texture feature vector {right arrow over (V)} texture ; (iii) wherein the subscene is in a YCbCr color space with a luminance Y channel, a blue chrominance Cb channel, and a red chrominance Cr channel; (f) wherein the color feature vector {right arrow over (V)} color is calculated for each image patch in the subscene by performing steps comprising: (i) calculating a mean value Y of the luminance Y channel and mean values Cb and red Cr respectively for both the blue Cb and red Cr chrominance channels; (ii) calculating a patch luminance variance as: Var ⁡ ( Y ) = 1 N ⁢ ∑ i = 1 N ⁢ ( Y i - Y _ ) 2 ; and (iii) calculating a patch chrominance variance as: Var ⁡ ( CbCr ) = 1 N ⁢ ( ∑ i = 1 N ⁢ ( Cb i - Cb _ ) 2 + ( Cr i - Cr _ ) 2 ) ; (iv) wherein N denotes a number of pixels in the image patch, Y i , Cb i , and Cr i denote a pixel value in position i in the image patch in each of the luminance Y channel, the blue Cb, and the red Cr chrominance channels, Y represents the mean value of Y, Cb represents the mean value of Cb, and Cr represents the mean value of Cr.