For instance, one can start by answering these questions: Therefore, it is important to try and forecast the possible use cases and the images that will be kept in the index in order to optimize the features. This may obviously not be the case for a real use case, where evaluation is done in a previous phase and the index is kept as efficient as possible. Choosing the featuresĭuring the feature extraction phase, we have extracted many possible features so that experimenting with different features becomes easier. The result images can be displayed as before. with _ha if using bit sampling or with _ms if using metric spaces).
#Apache lucene fuzzy search numfound full#
When using the select handler, we need to use the full field name (e.g.
#Apache lucene fuzzy search numfound update#
To make it simple, we can create a lire-config folder as a copy of the _default configuration from /opt/solr/server/solr/configsets/_default/, then update the managed-schema in the new configuration by adding these lines: Loading the feature documentsīefore we can load the features, we need to create a Solr index that can make use of them (and that exposes a LIRE request handler) we basically need text fields to index the hashes and the reference points and a special binary DocValues field to hold the encoded features. The XML file now contains a list of documents that can be indexed. -y: the additional features to extract in addition to the default PHOG, ColorLayout, EdgeHistogram and JCD (check the README file for a list of all the abbreviations).-a: an option to include both the bit sampling and the metric space representations.-i: the file containing the list of image files to process.$ java -cp dist/lire.jar:liresolr/dist/liresolr.jar .indexing.ParallelSolrIndexer -i val2014.txt -o val2014_all_plus_ms.xml -a -y "oh,sc,ce,fc,ac,ad,fo,jh" Once we have decided what features to use, we can use the ParallelSolrIndexer class to extract them and save them in an XML file: More details can be found in the LIRE documentation. The hashes and the reference objects can be used to speed up search by restricting the search space the candidate results returned by this phase are then re-ranked using the actual encoded feature vectors and a distance function associated with each feature. create reference objects to use with a distance function to enable nearest-neighbour search in metric spaces.extract hashes that represent the features via bit sampling).encode these features as Base64-encoded strings.
fuzzy opponent histogram (fuzzy color histogram in the opponent color space).opponent histogram (simple color histogram in the opponent color space).simple joint histogram (combines 64-bin RGB and pixel rank).AutoColorCorrelogram (color-to-color correlation histogram).ACCID (combines scaled edge and fuzzy color histograms).JCD (Joined Composite Descriptor, combines CEDD and FCTH).FCTH (Fuzzy Color and Texture Histogram).CEDD (Color and Edge Directivity Descriptor).PHOG (Pyramid Histogram of Oriented Gradients).
The features that LIRE can extract belong to both categories, but we will focus on the ones that are readily available in the Solr plugin as well (although other can be easily added), namely: Many features have been proposed in the computer vision and image processing literature broadly speaking, features can be classified as global when they describe global image properties (colors, edge histograms, etc.), and local when they describe small regions of an image (corners, edges, etc.). In order to create an index of the images, we need to extract some visual features and represent them as feature vectors.
Any directory can be used in its place.) Indexing (Here I am using /data as a root directory, which may require sudo rights to write to.
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