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While the problem of Content Based Image Retrieval (CBIR) and automated image indexing systems has been widely studied in the past years they still represent a challenging research field. Indeed capturing high level semantics from digital images basing on low level basic descriptors remains an issue. A review of existing systems shows that edge descriptors are among the most popular features. While color features have led to extensive work, edge features haven't produced such active research and most current systems rather rely on completing basic edge information with other, more computationally expensive features such as texture. In this paper we propose to work on a more accurate edge feature while keeping a relatively low computation cost. We will begin with a review of common edge features used in CBIR and automated indexing systems, we will then explain our Enhanced Fast Hough Transform algorithm and the edge descriptor we derived from it. Through a study of computational complexity, we will explain that the computational burden is kept minimal and experimental results using a sample automated indexing system will show that our new edge feature significantly improves over more traditional descriptors.