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We present our region-based image retrieval tool, finding region in the picture (FRIP), that is able to accommodate, to the extent possible, region scaling, rotation, and translation. Our goal is to develop an effective retrieval system to overcome a few limitations associated with existing systems. To do this, we propose adaptive circular filters used for semantic image segmentation, which are based on both Bayes' theorem and texture distribution of image. In addition, to decrease the computational complexity without losing the accuracy of the search results, we extract optimal feature vectors from segmented regions and apply them to our stepwise Boolean AND matching scheme. The experimental results using real world images show that our system can indeed improve retrieval performance compared to other global property-based or region-of-interest-based image retrieval methods.