Enhancing logo matching and recognition using local features

The design of visual feature extraction with scale invariant feature transform (SIFT) is widely used for recognition of object in logos. However, the real-time implementation suffers from heavy computation, and high memory storage, long latency because of its frame level computation, so we propose PCSIFT(principal component analysis SIFT) to over come all the drawback by recognition and matching multiple feature with multiple reference logos in real world application and in image archives by the help of designing a novel contribution framework. In Reference logos the test images are done on local features like, interest point, region etc. some terms of measuring feature like 1. feature matching quality is measured by the fidelity term, 2. feature co-occurrence/geometry is captured by neighbourhood criterian, 3. smoothness matching solution is done by the regularization term. We can use various logo set and real world logos to match and recognize the logos and the experimental result shows greater validity and accuracy.