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 Gray Levelmorphologically 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, Adobos 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.
