Accurate quantification of the integration strength between dynamically evolving markets has become a major issue in the context of the recent financial predicament, with the typical approaches relying mainly on the time-varying aspects of market indices. Despite its recognized virtue, incorporation of both temporal and frequency information has still gained limited attention in the framework of market integration. In this paper, a novel measure is proposed, which better adapts to the time-frequency content of market indices for quantifying the degree of their integration. To this end, advanced statistical signal processing techniques are employed to extract market interrelations not only across time, but also across frequency, thus distinguishing between short and long-term investors. Specifically, probabilistic principal component analysis is employed to extract the principal factors explaining the cross-market returns, while a Hough transformation, applied on appropriate time-scale wavelet decompositions of the original time series and the principal factors, is exploited to extract global patterns in the time-scale domain by detecting local features. Then, statistical divergence between the corresponding Hough transformed time-scale decompositions is used to quantify the degree of market integration. The efficiency of the proposed measure is evaluated on a set of 12 equity indices in the framework of well-diversified portfolio construction revealing an improved performance against alternative market integration measures, in terms of typical financial performance metrics.