1. A computer-implemented method for learning-based automatic commercial content detection, the method comprising: dividing program data into multiple segments; analyzing the segments to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content; wherein the context-based features are a function of one or more single-side left and/or right neighborhoods of segments of the multiple segments; and calculating context-based feature sets from segment-based visual features as an average value of visual features of S k , S k representing a set of all segments of the multiple segments that are partially or totally included in the single-side left and/or right neighborhoods such that S k ={C j k :0≦j<M k }={C i :C i ∩N k ≠ Φ}, M k being a number of segments in S k , and wherein N k represents 2n+1 neighborhoods, n represents a number of neighborhoods left and/or right of a current segment C i , S k is a set of segments that are partially or totally included in N k , C k i represents is a j-th element of S k , M k represents a total number of elements in S k , and Φ represents an empty set.