This paper describes a method for transforming local image features with relevant component analysis (RCA) for extracting a compact set of visual words from a large set of local image features. By applying this method to a set of local image features that distributed in chaos, the local image features are transformed to a proper number of well separated groups. By selecting a visual-word from each group, a compact set of visual words can be obtained that has much less ambiguity and redundancy than the un-transformed set of local features, thus the performance of object recognition can be improved much by using those visual words. We applied this method to the recognition of daily necessities and confirmed its effectiveness through several comparison experiments using common image database. The proposed method should be useful for supporting the daily life of disabled people.
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