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.

We present a supervised binary encoding scheme for image retrieval that learns projections by taking into account similarity between classes obtained from output embeddings. Our motivation is that binary hash codes learned in this way improve the visual quality of retrieval results by ranking related (or ``sibling'') class images before unrelated class images. We employ a sequential greedy optimization that learns relationship aware projections by minimizing the difference between inner products of binary codes and output embedding vectors. We develop a joint optimization framework to learn projections which improve the accuracy of supervised hashing over the current state of the art with respect to standard and sibling evaluation metrics. We further obtain discriminative features learned from correlations of kernelized input CNN features and output embeddings, which significantly boosts performance. Experiments are performed on three datasets: CUB-2011, SUN-Attribute and ImageNet ILSVRC 2010, where we show significant improvement in sibling performance metrics over state-of-the-art supervised hashing techniques, while maintaining performance with respect to standard metrics.

We present a hierarchical approach to compress large dimensional vectors using hyper spherical hashing functions. We provide a practical solution for learning hyper spherical hashing functions by partitioning the vectors and learning hyper spheres in subspaces. Our method is an efficient way to preserve the hashing properties of sub-space hashing functions to generate the full-hashing functions in a divide and conquer fashion. We demonstrate the performance of our approach on the ILSVRC2010 Validation dataset and two large scale datasets: ILSVRC2010 Train and Holidays+Flickr 1M with high dimensional representations of size 128000, 25600 and 12800 respectively. Our results highlight the compact nature of hyper spherical hashing functions which significantly outperform the state-of-the art methods at compression ratios of 512, 256 and 128. Furthermore, we boost the retrieval performance by introducing an as symetric distance for spherical hashing functions.

Current state-of-the-art systems for visual content analysis require large training sets for each class of interest, and performance degrades rapidly with fewer examples. In this paper, we present a general framework for the zeroshot learning problem of performing high-level event detection with no training exemplars, using only textual descriptions. This task goes beyond the traditional zero-shot framework of adapting a given set of classes with training data to unseen classes. We leverage video and image collections with free-form text descriptions from widely available web sources to learn a large bank of concepts, in addition to using several off-the-shelf concept detectors, speech, and video text for representing videos. We utilize natural language processing technologies to generate event description features. The extracted features are then projected to a common high-dimensional space using text expansion, and similarity is computed in this space. We present extensive experimental results on the large TRECVID MED [26] corpus to demonstrate our approach. Our results show that the proposed concept detection methods significantly outperform current attribute classifiers such as Classemes [34], ObjectBank [21], and SUN attributes[28] . Further, we find that fusion, both within as well as between modalities, is crucial for optimal performance.