Object and Scene Recognition Using Color Descriptors and Adaptive Color KLT
With the emergence and explosion of huge image databases there is an increasing necessity for effective methods to assess visual information on the level of objects and scene types. A wide variety of Content Based Image Retrieval (CBIR) systems already exists. As a key issue in CBIR, similarity measure quantifies the resemblance in contents between a pair of images. Depending on the type of features, the formulation of the similarity measure varies greatly. The primary goal of our study is to reduce the computation time and user interaction. The secondary goal is to reduce the semantic gap between high level concepts and low level features. A third goal is to evaluate system performance with regard to speed and accuracy. In the proposed study transform color after statistical transform, such as the Adaptive Color Karhunen Loeve Transform (ACKLT) is used as a color descriptor. The results are showing the advantage of the new algorithm for ACKLT in comparison with the YCrCb color model. Based on the experimental results, we concluded that correct selection of descriptors invariant to light intensity and light color changes affects object and scene category recognition.
HCI