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README.rst

README.rst

Video and Image Search Examples

Overview

This document corresponds to this folder online, in addition to the search_and_rapid_model_generation example folder in a VIAME installation.

This directory contains methods to accomplish two tasks:

(a) Performing exemplar-based searches on an archive of unannotated imagery or videos
(b) Quickly training up detection models for new categories of objects on the same ingest

Video and Image Archive Search using VIAME

Video archive search can be performed via a few methods. The default includes a pipeline which generates object detections, tracks, and lastly temporal descriptors around each track. The descriptors get indexed into an arbitrary data store (typically a nearest neighbor index, locality-sensitive hashing table, or other). At query time, descriptors on a query image or video are matched against the entries in this database. A default GUI (provided via the VIVIA toolkit) is provided which allows performing iterative refinement of the results, by annotating which were correct or incorrect, in order to build up a better model for the input query.

Image Archive Search using SMQTK Standalone

The "smqtk_on_chips" directory contains multiple methods for running image queries on an image archive, including:

(a) Indexing descriptors around each full input image as-is.
(b) Tiling up each input image into fixed-size tiles.
(c) Indexing descriptors around detections generated by arbitrary detectors.

|This primarily uses the SMQTK toolkit and is designed for images, not videos. A web-based GUI is provided which allows new queries based on an input query image, and the refinement of results via iterative query refinement (IQR), similarly to the VIAME example. IQR generates an SVM classifier on user-nominated positive and negative examples from the result set to refine results.

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