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This paper addresses the problem of object-based image retrieval, by using local feature extraction and a relevance feedback mechanism for quickly narrowing down the image search process to the user needs. This approach relies on the hypothesis that semantically similar images are clustered in some feature space and, in this scenario: (i) computes image signatures that are invariant to scale and rotation using SIFT, (ii) calculates the vector of locally aggregated descriptors (VLAD) to make a fixed length descriptor for the images, (iii) reduce the VLAD descriptor dimensionality with Principal Component Analysis (PCA) and (iv) uses the k-Means algorithm for grouping images that are semantically similar. The proposed approach has been successfully validated using 33,192 images from the ALOI database, obtaining a mean recall value of 47.4% for searches of images containing objects that are identical to the object query and 20.7% for searches of images containing different objects (albeit visually similar) to the object query.

Object-based Image Retrieval using Local Feature Extraction and Relevance Feedback