Logo-Recognition using Bundle Min-HashingAbhishek Mehra, Marshal Patel, Tejas Shah

Introduction Approach Results

Objective• The objective is to identify brand logos

from given set of images. The dataset consists of various images of objects bearing the logos. We first look for logo in the image and then try to classify it to a particular brand name.

• The technique is invariant to scale and builds an index using min-Hashing on the feature bundles. The feature bundles are formed using the spatial location and the visual word features.

• The idea is that these bundled features contain more information than a single feature.

Motivation and Application• One of the potential application is within

marketing industry where, the companies can track down the visibility of their logos within an image or video.

Bag of Words:

Bundle Min-Hashing:For each image, K-means computes centroids based on the location of SIFT

descriptors.

Bundles(group of sift descriptors) were created which includes sift descriptors in the fixed radius vicinity of each centroid.

For each bundle, a min-hash is computed and dictionaries are created.

Test image is processed to get min-hash. The min-hash is probed against the dictionaries and top 5 results are used to classify test image.

Total classes – 33.

Random guess = (1/33)*100 = 3.03%

Bag of Words:

References

[1] Romberg, Stefan, and Rainer Lienhart. "Bundle min-hashing for logo recognition." Proceedings of the 3rd ACM conference on International conference on multimedia retrieval. ACM, 2013.

Figure: Min-Hashing. Image courtesy [1]

12

90

23

43

0 1 2 3Visual Words

Histogram of Visual Words

VocabularySize

Accuracy

100 12%

250 13%

400 16%

VocabSize

No of Hash

Accuracy

100 25 8%

Bundle Min-Hashing: