In this paper, an object image retrieval method based on unsupervised SLIC is proposed. To represent the object in background, local texture feature as well as global edge and color features are extracted from the extracted object region to generate unified robust feature vectors. To show the effectiveness of our method, noisy object image retrieval using real and standard image databases were executed. The precision rates obtained by the cross-validation were calculated to evaluate the performance of image retrieval. High-performance object image retrieval was achieved compared with the content-based image retrieval method using the same types of combined robust features.
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