Semantic Binary Codes

Abstract

Fast Image Retrieval is required for many applications like Image Search and Shopping, especially for large datasets. Hashing addresses this problem by learning compact binary codes for images and using them as direct addresses into hash tables. In practice, using binary codes as addresses does not guarantee fast retrieval, as similar images are not mapped to the same binary code(address). We address this problem by presenting an efficient supervised hashing method that aims to explicitly map all images from the same class to a unique binary code to obtain fast retrieval. We refer to the binary codes of the images as 'Semantic Binary Codes' and the unique code for all same class images as 'Class Binary Code'. We formulate this intuitive objective 'directly' by minimizing the squared error criterion between the semantic binary codes and the corresponding class binary codes. We further propose a Deep Semantic Binary Code model that utilizes the class binary codes and show that we significantly outperform the state-of-the-art. We also propose a new class-based Hamming metric that dramatically reduces the retrieval times for larger databases and also improves the performance of the method by large margins.