Exemplar Dictionary Research Articles

Domain Adaptation and Semantic Drawing Driven Sketch-to-Photo Retrieval using Collaborative Generative Representation Learning

Abstract: Sketch-based face recognition is an interesting task in vision and multimedia research yet it is quite challenging due to the great difference between face photos and sketches. In this paper we propose a novel approach for photo-sketch generation aiming to automatically transform face photos into detail-preserving personal sketches. Unlike the traditional models synthesizing sketches based on a dictionary of exemplars we develop a fully convolutional network to learn the end-to-end photosketch mapping. Our approach takes whole face photos as inputs and directly generates the corresponding sketch images with efficient inference and learning in which the architecture is stacked by only convolutional kernels of very small sizes. The exemplar-based method is most frequently used in face sketch synthesis because of its efficiency in representing the nonlinear mapping between face photos and sketches. However, the sketches synthesized by existing exemplar-based methods suffer from block artifacts and blur effects. In addition, most exemplar-based methods ignore the training sketches in the weight representation process. To improve synthesis performance, a novel joint training model is proposed in this paper, taking sketches into consideration. First, we construct the joint training photo and sketch by concatenating the original photo and its sketch with a high-pass filtered image of their corresponding sketch. Then, an offline random sampling strategy is adopted for each test photo patch to select the joint training photo and sketch patches in the neighboring region. Finally, a novel locality constraint is designed to calculate the reconstruction weight, allowing the synthesized sketches to have more detailed information

Pedestrian Detection by Exemplar-Guided Contrastive Learning.

Typical methods for pedestrian detection focus on either tackling mutual occlusions between crowded pedestrians, or dealing with the various scales of pedestrians. Detecting pedestrians with substantial appearance diversities such as different pedestrian silhouettes, different viewpoints or different dressing, remains a crucial challenge. Instead of learning each of these diverse pedestrian appearance features individually as most existing methods do, we propose to perform contrastive learning to guide the feature learning in such a way that the semantic distance between pedestrians with different appearances in the learned feature space is minimized to eliminate the appearance diversities, whilst the distance between pedestrians and background is maximized. To facilitate the efficiency and effectiveness of contrastive learning, we construct an exemplar dictionary with representative pedestrian appearances as prior knowledge to construct effective contrastive training pairs and thus guide contrastive learning. Besides, the constructed exemplar dictionary is further leveraged to evaluate the quality of pedestrian proposals during inference by measuring the semantic distance between the proposal and the exemplar dictionary. Extensive experiments on both daytime and nighttime pedestrian detection validate the effectiveness of the proposed method.

Concept detection using text exemplars aligned with a specialized ontology

Knowledge extraction from text documents requires identifying and classifying semantic content. Utilizing an appropriate domain ontology can facilitate this process if words and phrases can be linked to the classes and relationships within the ontology. This paper presents an exemplar-based algorithm to link text to semantically similar classes within an ontology constructed for the chronic pain medicine domain. Human annotators linked classes to text segments within a random document set for construction of an exemplar dictionary, which we examined for completeness using Zipf plot analysis. An algorithm was created to use this dictionary on previously unseen text to form a map between sentence text and probable class assignments. We performed a 5×5 cross-validation between human and algorithm annotations and examined both ROC and precision versus recall curves to show that the algorithm can identify the many medical and biopsychosocial components from the texts. We briefly describe a use case for detecting pain relief from various interventions utilizing the word-by-class maps. We conclude that an exemplar-based method can be a valuable tool in knowledge extraction from texts that share similar construction, such as medical progress notes.

Sparse coding of i-vector/JFA latent vector over ensemble dictionaries for language identification systems

Computing sparse representation (SR) over an exemplar dictionary is time consuming and computationally expensive for large dictionary size. This also requires huge memory requirement for saving the dictionary. In order to reduce the latency and to achieve some diversity, ensemble of exemplar dictionary based language identification (LID) system is explored. The full diversity can be obtained if each of the exemplar dictionary contains only one feature vector from each of the language class. To achieve full diversity, a large number of multiple dictionaries are required; thus needs to compute SR for a particular test utterance as many times. The other solution to reduce the latency is to use a learned dictionary. The dictionary may contain unequal number of dictionary atoms and it is not guaranteed that each language class information is present. It totally depends upon the number of data and its variations. Motivated by this, language specific dictionary is learned, and then concatenated to form a single learned dictionary. Furthermore, to overcome the problem of ensemble exemplar dictionary based LID system, we investigated the ensemble of learned-exemplar dictionary based LID system. The proposed approach achieves the same diversity and latency as that of ensemble exemplar dictionary with reduced number of learned dictionaries. The proposed techniques are applied on two spoken utterance representations: the i-vector and the JFA latent vector. The experiments are performed on 2007 NIST LRE, 2009 NIST LRE and AP17-OLR datasets in closed set condition.

Evaluation, Language, and Untranslatables

The issue of translatability is pressing in international evaluation, in global transfer of evaluative instruments, in comparative performance management, and in culturally responsive evaluation. Terms that are never fully understood, digested, or accepted may continue to influence issues, problems, and social interactions in and around and after evaluations. Their meanings can be imposed or reinvented. Untranslatable terms are not just “lost in translation” but may produce overflows that do not go away. The purpose of this article is to increase attention to the issue of translatability in evaluation by means of specific exemplars. We provide a short dictionary of such exemplars delivered by evaluators, consultants, and teachers who work across a variety of contexts. We conclude with a few recommendations: highlight frictions in translatability by deliberately circulating and discussing words of relevance that appear to be “foreign”; increase the language skills of evaluators; and make research on frictions in translation an articulate part of the agenda for research on evaluation.

Source cell phone verification from speech recordings using sparse representation

Source recording device recognition is an important emerging research field in digital media forensics. The literature has mainly focused on the source recording device identification problem, whereas few studies have focused on the source recording device verification problem. Sparse representation based classification methods have shown promise for many applications. This paper proposes a source cell phone verification scheme based on sparse representation. It can be further divided into three schemes which utilize exemplar dictionary, unsupervised learned dictionary and supervised learned dictionary respectively. Specifically, the discriminative dictionary learned by supervised learning algorithm, which considers the representational and discriminative power simultaneously compared to the unsupervised learning algorithm, is utilized to further improve the performances of verification systems based on sparse representation. Gaussian supervectors (GSVs) based on MFCCs, which have shown to be effective in capturing the intrinsic characteristics of recording devices, are utilized for constructing and learning dictionary. SCUTPHONE, which is a corpus of speech recordings from 15 cell phones, is presented. Evaluation experiments are conducted on three corpora of speech recordings from cell phones and demonstrate the effectiveness of the proposed methods for cell phone verification. In addition, the influences of number of target examples in the exemplar dictionary and size of the unsupervised learned dictionary on source cell phone verification performance are also analyzed.

Multipitch Estimation of Piano Music by Exemplar-Based Sparse Representation

Pitch, together with other midlevel music features such as rhythm and timbre, holds the promise of bridging the semantic gap between low-level features and high-level semantics for music understanding. This paper investigates the pitch estimation of a piano music signal by exemplar-based sparse representation. A note exemplar is a segment of a piano note, stored in the dictionary. We first describe how to represent a segment of the piano music signal as a linear combination of a small number of note exemplars from a large note exemplar dictionary and then show how the sparse representation problem can be solved by -regularized minimization. The proposed approach incorporates tuning factor estimation, note candidate selection, and hidden-Markov-model-based smoothing into the estimation process to improve accuracy. Unlike previous approaches, the proposed approach does not require retraining for a new piano. Instead, only a dozen notes of the new piano are needed. This feature is computationally attractive and avoids intense manual labeling. The system performance is evaluated using 70 classical music recordings of two real pianos under different recording conditions. The results show that the proposed system outperforms four state-of-the-art systems.

Exemplar-Based Sparse Representations for Noise Robust Automatic Speech Recognition

This paper proposes to use exemplar-based sparse representations for noise robust automatic speech recognition. First, we describe how speech can be modeled as a linear combination of a small number of exemplars from a large speech exemplar dictionary. The exemplars are time-frequency patches of real speech, each spanning multiple time frames. We then propose to model speech corrupted by additive noise as a linear combination of noise and speech exemplars, and we derive an algorithm for recovering this sparse linear combination of exemplars from the observed noisy speech. We describe how the framework can be used for doing hybrid exemplar-based/HMM recognition by using the exemplar-activations together with the phonetic information associated with the exemplars. As an alternative to hybrid recognition, the framework also allows us to take a source separation approach which enables exemplar-based feature enhancement as well as missing data mask estimation. We evaluate the performance of these exemplar-based methods in connected digit recognition on the AURORA-2 database. Our results show that the hybrid system performed substantially better than source separation or missing data mask estimation at lower signal-to-noise ratios (SNRs), achieving up to 57.1% accuracy at SNR = -5 dB. Although not as effective as two baseline recognizers at higher SNRs, the novel approach offers a promising direction of future research on exemplar-based ASR.