Question $2$: What am I looking at$?(40\%)$

In this question, the aim is to identify an "unknown" object depicted in a query image, by matching it to multiple reference images, and selecting

the highest scoring match. Since we only have one reference image per object, there is at most one correct answer. This is useful for example if

you want to automatically identify a book from a picture of its cover, or a painting or a geographic location from an unlabelled photograph of it$.$

The steps are as follows:

Select a set of reference images and their corresponding query images.

Hint $1$: Start with the book covers, or just a subset of them.

Hint $2$: This question can require a lot of computation to run from start to finish, so cache intermediate results $($e$.$g$.$ feature

descriptors$)$ where you can.

Choose one query image corresponding to one of your reference images. Use RANSAC to match your query image to each reference

image, and count the number of inlier matches found in each case. This will be the matching score for that image.

Identify the query object. This is the identity of the reference image with the highest match score, or "not in dataset" if the maximum score

is below a threshold.

Repeat steps $2-3$ for every query image and report the overall accuracy of your method $($that is$,$ the percentage of query images that were

correctly matched in the dataset$).$ Discussion of results should include both overall accuracy and individual failure cases.

Hint $1$: In case of failure, what ranking did the actual match receive? If we used a "top$-$k$"$ accuracy measure, where a match is

considered correct if it appears in the top $k$ match scores, would that change the result?

$[23]$ # Your code to identify query objects and measure search accuracy for data set here

Your explanation of what you have done, and your results, here Choose some extra query images of objects that do not occur in the reference dataset. Repeat step $4$ with these images added to your

query set. Accuracy is now measured by the percentage of query images correctly identified in the dataset, or correctly identified as not

occurring in the dataset. Report how accuracy is altered by including these queries, and any changes you have made to improve

performance.

# Your code to run extra queries and display results here

# Include extra query images $($not in dataset$)$

Your explanation of results and any changes made here

Repeat step $4$ and $5$ for at least one other set of reference images from museum$\_$paintings or landmarks, and compare the accuracy

obtained. Analyse both your overall result and individual image matches to diagnose where problems are occurring, and what you could

do to improve performance. Test at least one of your proposed improvements and report its effect on accuracy.

$$

# Your code to search images and display results here

# Test with another set of reference images $($e$.$g$.,$ museum paintings$)$

Your description of what you have done, and explanation of results, here

Give python implementation of the criterion above. Give text explanation at the text area in the image attached. And also give visualisation like draw matches of set of reference and query images