Name
Abstract | ||
|---|---|---|
Most of the existing image retrieval systems take the textual query from the user and utilize the metadata associated with the database images to retrieve the result. However, the results of these systems are substandard. This paper presents a framework for the matching and retrieval of archaeological images based on highly specific visual features. As a result, the potential users of the system (archaeologists, art historians, curators and students) will have a tool to refine the search for digital images. The system incorporates the identification of distinct visual feature vectors, which are extracted from each image in the database through implementation of high level image feature based algorithms such as Robust Invariant Features (RIF), Scale Invariant Feature Transform (SIFT) and low level image feature based Hierarchical Tree Matching (HTM). The detailed comparison of these approaches, by reviewing their current capabilities, limitations, and potential usefulness in the field of archaeology is presented. |
Year | Venue | Field |
|---|---|---|
2008 | IPCV | Scale-invariant feature transform,Metadata,Feature vector,Computer science,Image retrieval,Digital image,Content based retrieval,Invariant (mathematics),Feature based,Archaeology |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akshay Asthana | 1 | 729 | 25.02 |
| Ranjan Dutta | 2 | 7 | 1.30 |
| Anshul Jain | 3 | 2 | 1.42 |
| Deepak Gupta | 4 | 1 | 4.13 |
| Sanjay Goel | 5 | 237 | 35.45 |