One-bit compressive sensing (CS) is an advanced version of sparse recovery in which the sparse signal of interest can be recovered from extremely quantized measurements. Namely, only the sign of each measurement is available to us. In many applications, the ground-truth signal is not sparse itself, but can be represented in a redundant dictionary. A strong line of research has addressed conventional CS in this signal model including its extension to one-bit measurements. However, one-bit CS suffers from the extremely large number of required measurements to achieve a predefined reconstruction error level. A common alternative to resolve this issue is to exploit adaptive schemes. Adaptive sampling acts on the acquired samples to trace the signal in an efficient way. In this work, we utilize an adaptive sampling strategy to recover dictionary-sparse signals from binary measurements. For this task, a multi-dimensional threshold is proposed to incorporate the previous signal estimates into the current sampling procedure. This strategy substantially reduces the required number of measurements for exact recovery. Our proof approach is based on the recent tools in high dimensional geometry in particular random hyperplane tessellation and Gaussian width. We show through rigorous and numerical analysis that the proposed algorithm considerably outperforms state of the art approaches. Further, our algorithm reaches an exponential error decay in terms of the number of quantized measurements.