8. A computing device comprising: a processor; and a memory coupled to the processor, the memory having encoded thereon a set of computer-program instructions executable by the processor, the computer-program instructions when executed by the processor for performing operations comprising: generating and ranking initial candidate annotations for a Web image, the ranking initial candidate annotations comprising utilizing a first ranking algorithm that obtains a first initial candidate annotation ranking and a second initial candidate annotation ranking for each initial candidate annotation and fuses the first initial candidate annotation ranking and the second initial candidate annotation ranking using a weighted linear combination scheme; searching a large-scale image database for search results, the search results comprising text and images related to the initial candidate annotations and corresponding image(s); clustering the search results based on the initial candidate annotations to create a set of clusters; selecting a set of extending candidate annotations based on names of the set of clusters; determining ranking values for each extending candidate annotation in the set of extending candidate annotations using a calculated average similarity between the Web image and images in a corresponding cluster; generating a bipartite graph using the initial candidate annotations as a first set of vertices disjoint from a second set of vertices representing the extending candidate annotations; reinforcing initial relevancy rankings of the initial candidate annotations and the extending candidate annotations by a reinforcement learning algorithm in view of relationships modeled by the bipartite graph between the two disjoint sets of vertices, the initial relevancy rankings indicating respective relevancy of each annotation of the initial candidate annotations and the extending candidate annotations to the Web image; re-ranking, by the reinforcement learning algorithm, the initial relevant rankings of initial candidate annotations, and the extending annotations based on the propagated relations between the vertices with weighted parameters to create re-ranked results; based on the re-ranked results annotating the Web image with a subset of the initial candidate annotations and the extending candidate annotations; generating an index from the subset; evaluating the index to present a search result to a search query from a user; and selecting the subset by evaluating re-ranked relevancy values generated by reinforcing the initial relevancy rankings, the evaluating comprising selecting a set of final annotations using a threshold algorithm that assigns higher weight to relevancy rankings associated with words in the initial candidate annotations.
8. A computing device comprising: a processor; and a memory coupled to the processor, the memory having encoded thereon a set of computer-program instructions executable by the processor, the computer-program instructions when executed by the processor for performing operations comprising: generating and ranking initial candidate annotations for a Web image, the ranking initial candidate annotations comprising utilizing a first ranking algorithm that obtains a first initial candidate annotation ranking and a second initial candidate annotation ranking for each initial candidate annotation and fuses the first initial candidate annotation ranking and the second initial candidate annotation ranking using a weighted linear combination scheme; searching a large-scale image database for search results, the search results comprising text and images related to the initial candidate annotations and corresponding image(s); clustering the search results based on the initial candidate annotations to create a set of clusters; selecting a set of extending candidate annotations based on names of the set of clusters; determining ranking values for each extending candidate annotation in the set of extending candidate annotations using a calculated average similarity between the Web image and images in a corresponding cluster; generating a bipartite graph using the initial candidate annotations as a first set of vertices disjoint from a second set of vertices representing the extending candidate annotations; reinforcing initial relevancy rankings of the initial candidate annotations and the extending candidate annotations by a reinforcement learning algorithm in view of relationships modeled by the bipartite graph between the two disjoint sets of vertices, the initial relevancy rankings indicating respective relevancy of each annotation of the initial candidate annotations and the extending candidate annotations to the Web image; re-ranking, by the reinforcement learning algorithm, the initial relevant rankings of initial candidate annotations, and the extending annotations based on the propagated relations between the vertices with weighted parameters to create re-ranked results; based on the re-ranked results annotating the Web image with a subset of the initial candidate annotations and the extending candidate annotations; generating an index from the subset; evaluating the index to present a search result to a search query from a user; and selecting the subset by evaluating re-ranked relevancy values generated by reinforcing the initial relevancy rankings, the evaluating comprising selecting a set of final annotations using a threshold algorithm that assigns higher weight to relevancy rankings associated with words in the initial candidate annotations. 11. The computing device of claim 8 , wherein the operations further comprise operations for ranking the initial candidate annotations with a first relevancy ranking algorithm that is different from a second relevancy ranking algorithm used to rank the extending candidate annotations.