IEEE Trans Image Process - Object segmentation of database images by dual multiscale morphological reconstructions and retrieval applications.

Tópicos

{ featur(1941) imag(1645) propos(1176) }
{ search(2224) databas(1162) retriev(909) }
{ measur(2081) correl(1212) valu(896) }
{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ method(1969) cluster(1462) data(1082) }
{ method(1557) propos(1049) approach(1037) }
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{ extract(1171) text(1153) clinic(932) }
{ care(1570) inform(1187) nurs(1089) }
{ sampl(1606) size(1419) use(1276) }
{ implement(1333) system(1263) develop(1122) }
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{ problem(2511) optim(1539) algorithm(950) }
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{ method(2212) result(1239) propos(1039) }

Resumo

Processing images for specific targets on a large scale has to handle various kinds of contents with regular processing steps. To segment objects in one image, we utilized dual multiScalE Graylevel mOrphological open and close recoNstructions (SEGON) to build a background (BG) gray-level variation mesh, which can help to identify BG and object regions. It was developed from a macroscopic perspective on image BG gray levels and implemented using standard procedures, thus robustly dealing with large-scale database images. The image segmentation capability of existing methods can be exploited by the BG mesh to improve object segmentation accuracy. To evaluate the segmentation accuracy, the probability of coherent segmentation labeling, i.e., the normalized probability random index (PRI), between a computer-segmented image and the hand-labeled one is computed for comparisons. Content-based image retrieval (CBIR) was carried out to evaluate the object segmentation capability in dealing with large-scale database images. Retrieval precision-recall (PR) and rank performances, with and without SEGON, were compared. For multi-instance retrieval with shape feature, AdaBoost was used to select salient common feature elements. For color features, the histogram intersection between two scalable HSV descriptors was calculated, and the mean feature vector was used for multi-instance retrieval. The distance measure for color feature can be adapted when both positive and negative queries are provided. The normalized correlation coefficient of features among query samples was computed to integrate the similarity ranks of different features in order to perform multi-instance with multifeature query. Experiments showed that the proposed object segmentation method outperforms others by 21% in the PRI. Performing SEGON-enabled CBIR on large-scale databases also improves on the PR performance reported elsewhere by up to 42% at a recall rate of 0.5. The proposed object segmentation method can be extended to extract other image features, and new feature types can be incorporated into the algorithm to further improve the image retrieval performance.

Resumo Limpo

process imag specif target larg scale handl various kind content regular process step segment object one imag util dual multiscal graylevel morpholog open close reconstruct segon build background bg graylevel variat mesh can help identifi bg object region develop macroscop perspect imag bg gray level implement use standard procedur thus robust deal largescal databas imag imag segment capabl exist method can exploit bg mesh improv object segment accuraci evalu segment accuraci probabl coher segment label ie normal probabl random index pri computerseg imag handlabel one comput comparison contentbas imag retriev cbir carri evalu object segment capabl deal largescal databas imag retriev precisionrecal pr rank perform without segon compar multiinst retriev shape featur adaboost use select salient common featur element color featur histogram intersect two scalabl hsv descriptor calcul mean featur vector use multiinst retriev distanc measur color featur can adapt posit negat queri provid normal correl coeffici featur among queri sampl comput integr similar rank differ featur order perform multiinst multifeatur queri experi show propos object segment method outperform other pri perform segonen cbir largescal databas also improv pr perform report elsewher recal rate propos object segment method can extend extract imag featur new featur type can incorpor algorithm improv imag retriev perform

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